10_time_series_forecasting_in_tensorflow.ipynb

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        "<a href=\"https://colab.research.google.com/github/mrdbourke/tensorflow-deep-learning/blob/main/10_time_series_forecasting_in_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# 10. Milestone Project 3: Time series forecasting in TensorFlow (BitPredict 💰📈)\n",
        "\n",
        "The goal of this notebook is to get you familiar with working with time series data.\n",
        "\n",
        "We're going to be building a series of models in an attempt to predict the price of Bitcoin. \n",
        "\n",
        "Welcome to Milestone Project 3, BitPredict 💰📈!\n",
        "\n",
        "> 🔑 **Note:** ⚠️ This is not financial advice, as you'll see time series forecasting for stock market prices is actually quite terrible.\n",
        "\n",
        "## What is a time series problem?\n",
        "\n",
        "Time series problems deal with data over time.\n",
        "\n",
        "Such as, the number of staff members in a company over 10-years, sales of computers for the past 5-years, electricity usage for the past 50-years.\n",
        "\n",
        "The timeline can be short (seconds/minutes) or long (years/decades). And the problems you might investigate using can usually be broken down into two categories.\n",
        "\n",
        "![example time series problems](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-example-time-series-problems.png)\n",
        "\n",
        "| Problem Type | Examples | Output |\n",
        "| ----- | ----- | ----- |\n",
        "| **Classification** | Anomaly detection, time series identification (where did this time series come from?) | Discrete (a label) |\n",
        "| **Forecasting** | Predicting stock market prices, forecasting future demand for a product, stocking inventory requirements | Continuous (a number) |\n",
        "\n",
        "In both cases above, a supervised learning approach is often used. Meaning, you'd have some example data and a label assosciated with that data.\n",
        "\n",
        "For example, in forecasting the price of Bitcoin, your data could be the historical price of Bitcoin for the past month and the label could be today's price (the label can't be tomorrow's price because that's what we'd want to predict).\n",
        "\n",
        "Can you guess what kind of problem BitPredict 💰📈 is?\n",
        "\n",
        "## What we're going to cover\n",
        "\n",
        "Are you ready?\n",
        "\n",
        "We've got a lot to go through. \n",
        "\n",
        "* Get time series data (the historical price of Bitcoin)\n",
        "  * Load in time series data using pandas/Python's CSV module\n",
        "* Format data for a time series problem\n",
        "  * Creating training and test sets (the wrong way)\n",
        "  * Creating training and test sets (the right way)\n",
        "  * Visualizing time series data\n",
        "  * Turning time series data into a supervised learning problem (windowing)\n",
        "  * Preparing univariate and multivariate (more than one variable) data\n",
        "* Evaluating a time series forecasting model\n",
        "* Setting up a series of deep learning modelling experiments\n",
        "  * Dense (fully-connected) networks\n",
        "  * Sequence models (LSTM and 1D CNN)\n",
        "  * Ensembling (combining multiple models together)\n",
        "  * Multivariate models\n",
        "  * Replicating the N-BEATS algorithm using TensorFlow layer subclassing\n",
        "* Creating a modelling checkpoint to save the best performing model during training\n",
        "* Making predictions (forecasts) with a time series model\n",
        "* Creating prediction intervals for time series model forecasts\n",
        "* Discussing two different types of uncertainty in machine learning (data uncertainty and model uncertainty)\n",
        "* Demonstrating why forecasting in an open system is BS (the turkey problem) \n",
        "\n",
        "## How you can use this notebook\n",
        "You can read through the descriptions and the code (it should all run), but there's a better option.\n",
        "\n",
        "Write all of the code yourself.\n",
        "\n",
        "Yes. I'm serious. Create a new notebook, and rewrite each line by yourself. Investigate it, see if you can break it, why does it break?\n",
        "\n",
        "You don't have to write the text descriptions but writing the code yourself is a great way to get hands-on experience.\n",
        "\n",
        "Don't worry if you make mistakes, we all do. The way to get better and make less mistakes is to **write more code**.\n",
        "\n",
        "> 📖 **Resource:** Get all of the materials you need for this notebook on the [course GitHub](https://github.com/mrdbourke/tensorflow-deep-learning).\n",
        "\n"
      ]
    },
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      "cell_type": "markdown",
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      "source": [
        "## Check for GPU\n",
        "\n",
        "In order for our deep learning models to run as fast as possible, we'll need access to a GPU.\n",
        "\n",
        "In Google Colab, you can set this up by going to Runtime -> Change runtime type -> Hardware accelerator -> GPU.\n",
        "\n",
        "After selecting GPU, you may have to restart the runtime."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjEuJ-j7yEdK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49462ae4-592f-4240-f8a2-633308ac4022"
      },
      "source": [
        "# Check for GPU\n",
        "!nvidia-smi -L"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla K80 (UUID: GPU-c7456639-4229-1150-8316-e4197bf2c93e)\n"
          ]
        }
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      "source": [
        "## Get data\n",
        "\n",
        "To build a time series forecasting model, the first thing we're going to need is data.\n",
        "\n",
        "And since we're trying to predict the price of Bitcoin, we'll need Bitcoin data.\n",
        "\n",
        "Specifically, we're going to get the prices of Bitcoin from 01 October 2013 to 18 May 2021.\n",
        "\n",
        "Why these dates?\n",
        "\n",
        "Because 01 October 2013 is when our data source ([Coindesk](https://www.coindesk.com/price/bitcoin)) started recording the price of Bitcoin and 18 May 2021 is when this notebook was created.\n",
        "\n",
        "If you're going through this notebook at a later date, you'll be able to use what you learn to predict on later dates of Bitcoin, you'll just have to adjust the data source.\n",
        "\n",
        "> 📖 **Resource:** To get the Bitcoin historical data, I went to the [Coindesk page for Bitcoin prices](https://www.coindesk.com/price/bitcoin), clicked on \"all\" and then clicked on \"Export data\" and selected \"CSV\". \n",
        "\n",
        "You can find the data we're going to use on [GitHub](https://github.com/mrdbourke/tensorflow-deep-learning/blob/main/extras/BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVEjK6ZHDqkQ",
        "outputId": "496c7595-6eda-4c17-a1a7-99fc4c4398b8"
      },
      "source": [
        "# Download Bitcoin historical data from GitHub \n",
        "# Note: you'll need to select \"Raw\" to download the data in the correct format\n",
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv "
      ],
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            "--2021-09-27 03:40:22--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 178509 (174K) [text/plain]\n",
            "Saving to: ‘BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv’\n",
            "\n",
            "BTC_USD_2013-10-01_ 100%[===================>] 174.33K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2021-09-27 03:40:22 (16.5 MB/s) - ‘BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv’ saved [178509/178509]\n",
            "\n"
          ]
        }
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      "cell_type": "markdown",
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        "id": "FRKabu6qB41C"
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      "source": [
        "### Importing time series data with pandas\n",
        "Now we've got some data to work with, let's import it using pandas so we can visualize it.\n",
        "\n",
        "Because our data is in **CSV (comma separated values)** format (a very common data format for time series), we'll use the pandas [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) function.\n",
        "\n",
        "And because our data has a date component, we'll tell pandas to parse the dates using the `parse_dates` parameter passing it the name our of the date column (\"Date\")."
      ]
    },
    {
      "cell_type": "code",
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          "height": 235
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        "id": "5FOrshVX_dUE",
        "outputId": "cac2d499-37ce-42d1-b0e6-ba3562918962"
      },
      "source": [
        "# Import with pandas \n",
        "import pandas as pd\n",
        "# Parse dates and set date column to index\n",
        "df = pd.read_csv(\"/content/BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv\", \n",
        "                 parse_dates=[\"Date\"], \n",
        "                 index_col=[\"Date\"]) # parse the date column (tell pandas column 1 is a datetime)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Currency</th>\n",
              "      <th>Closing Price (USD)</th>\n",
              "      <th>24h Open (USD)</th>\n",
              "      <th>24h High (USD)</th>\n",
              "      <th>24h Low (USD)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>BTC</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>124.30466</td>\n",
              "      <td>124.75166</td>\n",
              "      <td>122.56349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>BTC</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>125.75850</td>\n",
              "      <td>123.63383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>BTC</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>125.66566</td>\n",
              "      <td>83.32833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>BTC</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>118.67500</td>\n",
              "      <td>107.05816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>BTC</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>121.93633</td>\n",
              "      <td>118.00566</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Currency  Closing Price (USD)  ...  24h High (USD)  24h Low (USD)\n",
              "Date                                      ...                               \n",
              "2013-10-01      BTC            123.65499  ...       124.75166      122.56349\n",
              "2013-10-02      BTC            125.45500  ...       125.75850      123.63383\n",
              "2013-10-03      BTC            108.58483  ...       125.66566       83.32833\n",
              "2013-10-04      BTC            118.67466  ...       118.67500      107.05816\n",
              "2013-10-05      BTC            121.33866  ...       121.93633      118.00566\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQiDyqJaCdSS"
      },
      "source": [
        "Looking good! Let's get some more info."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-YoeINjCzJ_",
        "outputId": "d4ac2c82-9f59-438a-c749-fe418b04764d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 2787 entries, 2013-10-01 to 2021-05-18\n",
            "Data columns (total 5 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Currency             2787 non-null   object \n",
            " 1   Closing Price (USD)  2787 non-null   float64\n",
            " 2   24h Open (USD)       2787 non-null   float64\n",
            " 3   24h High (USD)       2787 non-null   float64\n",
            " 4   24h Low (USD)        2787 non-null   float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 130.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4s00kcEDCrjV"
      },
      "source": [
        "Because we told pandas to parse the date column and set it as the index, its not in the list of columns.\n",
        "\n",
        "You can also see there isn't many samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XP3cN0uvlxhi",
        "outputId": "39e39718-290e-4d99-f62a-8ec042714e65"
      },
      "source": [
        "# How many samples do we have?\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2787"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1RV6gYxC_Rs"
      },
      "source": [
        "We've collected the historical price of Bitcoin for the past ~8 years but there's only 2787 total samples.\n",
        "\n",
        "This is something you'll run into with time series data problems. Often, the number of samples isn't as large as other kinds of data.\n",
        "\n",
        "For example, collecting one sample at different time frames results in:\n",
        "\n",
        "| 1 sample per timeframe | Number of samples per year |\n",
        "| ----- | ----- |\n",
        "| Second | 31,536,000 |\n",
        "| Hour | 8,760 |\n",
        "| Day | 365 |\n",
        "| Week | 52 |\n",
        "| Month | 12 |\n",
        "\n",
        "> 🔑 **Note:** The frequency at which a time series value is collected is often referred to as **seasonality**. This is usually mesaured in number of samples per year. For example, collecting the price of Bitcoin once per day would result in a time series with a seasonality of 365. Time series data collected with different seasonality values often exhibit seasonal patterns (e.g. electricity demand behing higher in Summer months for air conditioning than Winter months). For more on different time series patterns, see [Forecasting: Principles and Practice Chapter 2.3](https://otexts.com/fpp3/tspatterns.html).\n",
        "\n",
        "![different types of time series patterns](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-types-of-time-series-patterns.png)\n",
        "*Example of different kinds of patterns you'll see in time series data. Notice the bottom right time series (Google stock price changes) has little to no patterns, making it difficult to predict. See [Forecasting: Principles and Practice Chapter 2.3](https://otexts.com/fpp3/tspatterns.html) for full graphic.*\n",
        "\n",
        "Deep learning algorithms usually flourish with lots of data, in the range of thousands to millions of samples.\n",
        "\n",
        "In our case, we've got the daily prices of Bitcoin, a max of 365 samples per year.\n",
        "\n",
        "But that doesn't we can't try them with our data.\n",
        "\n",
        "To simplify, let's remove some of the columns from our data so we're only left with a date index and the closing price."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "QxoNCv2NCuLY",
        "outputId": "0fce77d2-7d89-4c68-c26b-c7b6750efc44"
      },
      "source": [
        "# Only want closing price for each day \n",
        "bitcoin_prices = pd.DataFrame(df[\"Closing Price (USD)\"]).rename(columns={\"Closing Price (USD)\": \"Price\"})\n",
        "bitcoin_prices.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>123.65499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>125.45500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>108.58483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>118.67466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>121.33866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                Price\n",
              "Date                 \n",
              "2013-10-01  123.65499\n",
              "2013-10-02  125.45500\n",
              "2013-10-03  108.58483\n",
              "2013-10-04  118.67466\n",
              "2013-10-05  121.33866"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olHSR6poID_w"
      },
      "source": [
        "Much better!\n",
        "\n",
        "But that's only five days worth of Bitcoin prices, let's plot everything we've got."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "CfkhZhmCEyUe",
        "outputId": "c49b5f77-3059-416d-8a13-9ae7f2768ebf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "bitcoin_prices.plot(figsize=(10, 7))\n",
        "plt.ylabel(\"BTC Price\")\n",
        "plt.title(\"Price of Bitcoin from 1 Oct 2013 to 18 May 2021\", fontsize=16)\n",
        "plt.legend(fontsize=14);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wndwxoo_IZry"
      },
      "source": [
        "Woah, looks like it would've been a good idea to buy Bitcoin back in 2014."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2C1JsHUPYGG"
      },
      "source": [
        "### Importing time series data with Python's CSV module\n",
        "\n",
        "If your time series data comes in CSV form you don't necessarily have to use pandas.\n",
        "\n",
        "You can use Python's [in-built `csv` module](https://docs.python.org/3/library/csv.html). And if you're working with dates, you might also want to use Python's [`datetime`](https://docs.python.org/3/library/datetime.html).\n",
        "\n",
        "Let's see how we can replicate the plot we created before except this time using Python's `csv` and `datetime` modules.\n",
        "\n",
        "> 📖 **Resource:** For a great guide on using Python's `csv` module, check out Real Python's tutorial on [Reading and Writing CSV files in Python](https://realpython.com/python-csv/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UkTvwP2DCuGK",
        "outputId": "9bf2e5c1-fea5-44a2-e247-71a72645848c"
      },
      "source": [
        "# Importing and formatting historical Bitcoin data with Python\n",
        "import csv\n",
        "from datetime import datetime\n",
        "\n",
        "timesteps = []\n",
        "btc_price = []\n",
        "with open(\"/content/BTC_USD_2013-10-01_2021-05-18-CoinDesk.csv\", \"r\") as f:\n",
        "  csv_reader = csv.reader(f, delimiter=\",\") # read in the target CSV\n",
        "  next(csv_reader) # skip first line (this gets rid of the column titles)\n",
        "  for line in csv_reader:\n",
        "    timesteps.append(datetime.strptime(line[1], \"%Y-%m-%d\")) # get the dates as dates (not strings), strptime = string parse time\n",
        "    btc_price.append(float(line[2])) # get the closing price as float\n",
        "\n",
        "# View first 10 of each\n",
        "timesteps[:10], btc_price[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([datetime.datetime(2013, 10, 1, 0, 0),\n",
              "  datetime.datetime(2013, 10, 2, 0, 0),\n",
              "  datetime.datetime(2013, 10, 3, 0, 0),\n",
              "  datetime.datetime(2013, 10, 4, 0, 0),\n",
              "  datetime.datetime(2013, 10, 5, 0, 0),\n",
              "  datetime.datetime(2013, 10, 6, 0, 0),\n",
              "  datetime.datetime(2013, 10, 7, 0, 0),\n",
              "  datetime.datetime(2013, 10, 8, 0, 0),\n",
              "  datetime.datetime(2013, 10, 9, 0, 0),\n",
              "  datetime.datetime(2013, 10, 10, 0, 0)],\n",
              " [123.65499,\n",
              "  125.455,\n",
              "  108.58483,\n",
              "  118.67466,\n",
              "  121.33866,\n",
              "  120.65533,\n",
              "  121.795,\n",
              "  123.033,\n",
              "  124.049,\n",
              "  125.96116])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40o59gpNKtRs"
      },
      "source": [
        "Beautiful! Now, let's see how things look."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "bTSX7RZ9Cytj",
        "outputId": "2ed83ea1-2136-4890-93b7-f94ffdb50870"
      },
      "source": [
        "# Plot from CSV\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.plot(timesteps, btc_price)\n",
        "plt.title(\"Price of Bitcoin from 1 Oct 2013 to 18 May 2021\", fontsize=16)\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"BTC Price\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G12TARVyK_gN"
      },
      "source": [
        "Ho ho! Would you look at that! Just like the pandas plot. And because we formatted the `timesteps` to be `datetime` objects, `matplotlib` displays a fantastic looking date axis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nfP7i9R_dOp"
      },
      "source": [
        "## Format Data Part 1: Creatining train and test sets for time series data\n",
        "\n",
        "Alrighty. What's next?\n",
        "\n",
        "If you guessed preparing our data for a model, you'd be right.\n",
        "\n",
        "What's the most important first step for preparing any machine learning dataset?\n",
        "\n",
        "Scaling?\n",
        "\n",
        "No...\n",
        "\n",
        "Removing outliers?\n",
        "\n",
        "No...\n",
        "\n",
        "How about creating train and test splits?\n",
        "\n",
        "Yes! \n",
        "\n",
        "Usually, you could create a train and test split using a function like Scikit-Learn's outstanding [`train_test_split()`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) but as we'll see in a moment, this doesn't really cut it for time series data.\n",
        "\n",
        "But before we do create splits, it's worth talking about what *kind* of data we have.\n",
        "\n",
        "In time series problems, you'll either have **univariate** or **multivariate** data.\n",
        "\n",
        "Can you guess what our data is?\n",
        "\n",
        "* **Univariate** time series data deals with *one* variable, for example, using the price of Bitcoin to predict the price of Bitcoin.\n",
        "* **Multivariate** time series data deals with *more than one* variable, for example, predicting electricity demand using the day of week, time of year and number of houses in a region.\n",
        "\n",
        "![comparison of univariate and multivariate time series data](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-univariate-and-multivariate-time-series-data.png)\n",
        "*Example of univariate and multivariate time series data. Univariate involves using the target to predict the target. Multivariate inolves using the target as well as another time series to predict the target.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prkQhgiHNOKd"
      },
      "source": [
        "### Create train & test sets for time series (the wrong way)\n",
        "\n",
        "Okay, we've figured out we're dealing with a univariate time series, so we only have to make a split on one variable (for multivariate time series, you will have to split multiple variables). \n",
        "\n",
        "How about we first see the *wrong way* for splitting time series data?\n",
        "\n",
        "Let's turn our DataFrame index and column into NumPy arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxQbuGVTRFKX",
        "outputId": "b394ae69-cd85-42cc-ded1-d8e1cc1ab727"
      },
      "source": [
        "# Get bitcoin date array\n",
        "timesteps = bitcoin_prices.index.to_numpy()\n",
        "prices = bitcoin_prices[\"Price\"].to_numpy()\n",
        "\n",
        "timesteps[:10], prices[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['2013-10-01T00:00:00.000000000', '2013-10-02T00:00:00.000000000',\n",
              "        '2013-10-03T00:00:00.000000000', '2013-10-04T00:00:00.000000000',\n",
              "        '2013-10-05T00:00:00.000000000', '2013-10-06T00:00:00.000000000',\n",
              "        '2013-10-07T00:00:00.000000000', '2013-10-08T00:00:00.000000000',\n",
              "        '2013-10-09T00:00:00.000000000', '2013-10-10T00:00:00.000000000'],\n",
              "       dtype='datetime64[ns]'),\n",
              " array([123.65499, 125.455  , 108.58483, 118.67466, 121.33866, 120.65533,\n",
              "        121.795  , 123.033  , 124.049  , 125.96116]))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0jPVUNztInE"
      },
      "source": [
        "And now we'll use the ever faithful `train_test_split` from Scikit-Learn to create our train and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGY7hTfyNLZ1",
        "outputId": "327b70cb-fd4b-4e2b-d6d3-426b136c06f3"
      },
      "source": [
        "# Wrong way to make train/test sets for time series\n",
        "from sklearn.model_selection import train_test_split \n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(timesteps, # dates\n",
        "                                                    prices, # prices\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2229,), (558,), (2229,), (558,))"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJbMDz1stTxZ"
      },
      "source": [
        "Looks like the splits worked well, but let's not trust numbers on a page, let's visualize, visualize, visualize!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "cvH_0RvSNLWm",
        "outputId": "b0ae49a7-b184-49a0-b83f-0167e6bb776b"
      },
      "source": [
        "# Let's plot wrong train and test splits\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X_train, y_train, s=5, label=\"Train data\")\n",
        "plt.scatter(X_test, y_test, s=5, label=\"Test data\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"BTC Price\")\n",
        "plt.legend(fontsize=14)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OK9sORtjRh5z"
      },
      "source": [
        "Hmmm... what's wrong with this plot?\n",
        "\n",
        "Well, let's remind ourselves of what we're trying to do.\n",
        "\n",
        "We're trying to use the historical price of Bitcoin to predict future prices of Bitcoin.\n",
        "\n",
        "With this in mind, our seen data (training set) is what?\n",
        "\n",
        "Prices of Bitcoin in the past.\n",
        "\n",
        "And our unseen data (test set) is?\n",
        "\n",
        "Prices of Bitcoin in the future. \n",
        "\n",
        "Does the plot above reflect this?\n",
        "\n",
        "No. \n",
        "\n",
        "Our test data is scattered all throughout the training data.\n",
        "\n",
        "This kind of random split is okay for datasets without a time component (such as images or passages of text for classification problems) but for time series, we've got to take the time factor into account.\n",
        "\n",
        "To fix this, we've got to split our data in a way that reflects what we're actually trying to do.\n",
        "\n",
        "We need to split our historical Bitcoin data to have a dataset that reflects the past (train set) and a dataset that reflects the future (test set).\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajoASBK2size"
      },
      "source": [
        "### Create train & test sets for time series (the right way)\n",
        "\n",
        "Of course, there's no way we can actually access data from the future.\n",
        "\n",
        "But we can engineer our test set to be in the future with respect to the training set.\n",
        "\n",
        "To do this, we can create an abitrary point in time to split our data.\n",
        "\n",
        "Everything before the point in time can be considered the training set and everything after the point in time can be considered the test set.\n",
        "\n",
        "![time series train test split](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-time-series-train-test-split.png)\n",
        "*Demonstration of time series split. Rather than a traditionaly random train/test split, it's best to split the time series data sequentially. Meaning, the test data should be data from the future when compared to the training data.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpEVKyXANLT4",
        "outputId": "94acf2dc-837b-4b73-aa37-63eefe8ffadd"
      },
      "source": [
        "# Create train and test splits the right way for time series data\n",
        "split_size = int(0.8 * len(prices)) # 80% train, 20% test\n",
        "\n",
        "# Create train data splits (everything before the split)\n",
        "X_train, y_train = timesteps[:split_size], prices[:split_size]\n",
        "\n",
        "# Create test data splits (everything after the split)\n",
        "X_test, y_test = timesteps[split_size:], prices[split_size:]\n",
        "\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2229, 558, 2229, 558)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByN_YCotv3zk"
      },
      "source": [
        "Okay, looks like our custom made splits are the same lengths as the splits we made with `train_test_split`.\n",
        "\n",
        "But again, these are numbers on a page.\n",
        "\n",
        "And you know how the saying goes, trust one eye more than two ears.\n",
        "\n",
        "Let's visualize."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "L9QImNlhOQuS",
        "outputId": "a0eae767-fea6-44df-b68c-b63baa60cc49"
      },
      "source": [
        "# Plot correctly made splits\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.scatter(X_train, y_train, s=5, label=\"Train data\")\n",
        "plt.scatter(X_test, y_test, s=5, label=\"Test data\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"BTC Price\")\n",
        "plt.legend(fontsize=14)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VH/3oRxk4cCDDhg1jypQpPPbYY5x77rkMGDCAW265hQMHDvTtH5yIiGSf2jcTB2+jpsOlz7QPxhSWwri5fvA2bm5SKzOk2JYKOVxOP/10nnzySZ577jnmzp3L3Llz+f73v8+ECRMAGDJkCH/72984++yzmTx5Ml/96lf59re/zWc+8xkAzj//fM4880wWLFhAaWkpDz/8cMLPueiii7j55pu5/vrrmTlzJq+//jrXXntt3D3Lli2joaGB2bNnc/HFF3PZZZd1qtTwwx/+kOeee47x48czc+ZMAO644w5GjhzJSSedxMKFCykrK+Okk046zH9SIiKS9UqPgbzi9vMBhf5r1WvxpbWi2SUMP6hLYrYJc0n88GSYM2eOq6ioSHht06ZNTJ06tZ97JP1Ff78iIpKQ58H9Z0LlaiidBjWbgJj46CubYMgYP0XYHVP9KdRQLly7qc9n3MxsnXOu04ZQjcCJiIhIdmuohu1B3YGajRDqsGRo/x4/yHMuZYrbaw2ciIiIZC/Pg+WXgvNi2mLWW+cVQ+nR7btPx831i9sXj0pqGhGNwImIiEj2iu4sTcjgixWwf3f77tNtQaaFJAZvoABOREREslG07GX+cMgLNi0MKIIx0eVmBhNP8Efa8kdAzqDoG+Enc/zUI0mkKdQOnHNYkqNqOfw8z+v5JhERyQ6xCXnHzPST9QJEDsAlvw5uMn+DgnNQ+3dobWh/f0s91GyG0ckrBqUALsagQYOoq6tjxIgRCuIyhHOO1tZWqqqqKCwsTHZ3REQkFcQm5N2+DvIKoKUhKJtVArlBeOR58MBZ/rRpbgGEm9qfUVCSnL4HFMDFGDduHNu3b6empibZXZHDKDc3lyFDhlBSktz/2EREJEXElr0cM9MP4sAP4h44Ay571j+v3gjbXvSPw00wchpUb4axs5KesF954ERERCT7RIvSF5TAstNg+0t+u+XAVX+Fp74BW1cDXSzBmXACLHmyz2uhKg+ciIiISFS07GUoBEuegryi9ms/Pxm2vkh78JYgXNoe1EJNEgVwIiIikt0O7IHW/f6xi/g/OLBQe4mt2FJbGIwvUyJfERERkX4TnT4tLPXzuRWWwoQyf7NCXqG/K3XcXDjzdlh6MuD5bf/3b5A/DPa/DyOnJjUXnAI4ERERyR6xKUTGz4PFK/xp1MUrgjVxI6Cprn10LbrZYfw8P2h78JzO700CBXAiIiKSPWJTiFQG69iia+GKRvoBXpRZe2BXWAqNNYnfmwRaAyciIiLZI5pCJFFB+ujo3B1T/fxvntce2EWnWlXMXkRERKSfdRxVi13H1tXoHLSvm1v8RPsUq4rZi4iIiPSTUMjP/9ZY45fKisof4Sf27TjCFjsy9+A5/ntVzF5ERESkHyWaKvU8eOgc2LHeD+IWPdEepDXV+jtUvbD/msT8b1EK4ERERCS7dJwqbaz2i9NXrvFzwO1YHx+kFYzw04uA/1owIjn9jqEATkRERLJL7GaEcXNh+WXwsxMhN9+/7jxYfmn7jtSmOj8PHPivTXXJ6XcMbWIQERGR7GIGix6H2jchfzj86Fh/5K2lPrjBtW9iKCjx18nF5oNL4u7TKAVwIiIikl08Dx76Fz8gGzfX/4lOn0aNmelPlUaT/o6bC/+2AYpHJX0DAyiAExERkWwTuwZu+1r4ygbA/GnTbeUwdhZc9mzn+0KhlAjeQAGciIiIZJvoGrjolGjRqMT54aL3bSv3R+Tyh0NDddJzwAGYi81/kgXmzJnjKioqkt0NERERSaaOBe27EgnD/Wf4O1Ojhe77sQ6qma1zzs3p2K5dqCIiIpJ9YktkdWf/bnjvZX99XPO++CoNSaQATkRERKQrhaX+BoY25p8neSeqAjgRERER8KdVG6rjy2uZwQX3QygnOA/BhQ8kfQ2cAjgRERGRROW1oopHwfgyP/HvhLL2AvdJpF2oIiIiIh3LazXVtgdqiXaoJplG4ERERERiy2slqrbQ200P/UQjcCIiIpJdEqUQiY6yNVYD3QRpvU0/0sc0AiciIiLZo7u1buAXtr9zWvy16OYGL9L9e/uRRuBEREQkezTV+pUVXMR/jV3rlmgdXEFJez3UMTP9nHCJ1sn1M43AiYiISPYoGOFXVAD/NX9Ye+qQROvgYoO6Hev9IK6rdXL9SCNwIiIikj2a6vxyWAAtjXD/Qn9ULVoeq6t6qNG6qYuf8J+R5DVwCuBEREQke8QGZF1NicZOiyZKIaI8cCIiIiL9KDYgi13f1t2UaDSFSApRACciIiLZJTYgW/Q41L4JpcekTI633tAmBhEREclOngcPngM/O7H7tCCRMFRtTGrakI40AiciIiLZqbEatr3oH2970T8vHh1/TyQMtx0Jzftg4GD4xjuQk/zwSSNwIiIikqWM9qoLsccxat/0gzfwX2vf7Ke+da9PAzgze9fMXjezV8ysImgbbmYrzeyt4HVY0G5mdreZbTGz18xsVsxzFgf3v2Vmi2PaZwfP3xK8N30mr0VERCS5ikbChPkQyoGJJyTeqFB6jD/yBv5r6THtlRmc69/+xuiPMcB/ds7Vxpx/E1jlnPu+mX0zOP93YCEwOfiZB9wDzDOz4cBNwBzAAevM7HHn3J7gniuANcAfgTOAp/rhO4mIiEi6M4MlT3Zf2zQU8qdNoxsdIH7n6uIV/j39LBlTqOcCDwbHDwLnxbQ/5HzlwFAzOwI4HVjpnNsdBG0rgTOCa4Odc+XOOQc8FPMsERERkZ5Fd6R2N4mXkwujpvn3NlRDZXl87rgk6OsAzgHPmtk6M7syaBvlnNsZHO8CRgXHY4HKmPduD9q6a9+eoL0TM7vSzCrMrKKmpuZQvo+IiIhkK8+D5Zf6Re0xGDc3aeW0+noK9UTn3A4zGwmsNLPNsRedc87M+nwC2Tm3FFgKMGfOnORNWIuIiEj6aqqF7Wv9YwvBhQ8kLXdcn47AOed2BK/VwGPAXKAqmP4keK0Obt8BjI95+7igrbv2cQnaRURERBKL3YDgeVC/C+qrerchIbbY/YSypFZn6LMRODMrBELOufrg+DTgVuBxYDHw/eD1D8FbHge+aGaP4G9i2Ouc22lmzwDfje5WDZ5znXNut5ntM7My/E0Mi4Af99X3ERERkTTnee0bEMbN9YO2ytWA+btRlzzZ/YaERHVRk6Qvp1BHAY8FmT1ygd845542s5eA35nZ5cBW4JPB/X8EzgS2AE3ApQBBoPYd4KXgvludc7uD46uBB4B8/N2n2oEqIiIiiTXV+sGbF4ZtQeAGgPM3JkSL2XcnReqi9lkA55z7BzAjQXsdsCBBuwO+0MWzlgHLErRXAMcdcmdFREQk8xWW+iNv217E32cZM22aVwQFI5LVsw9MlRhEREQkO5jBBff7GxA6am2CprrE70uBxL0dKYATERGR7FE8CsaXtZ/nFfuVGMbPS5wSJLpu7o6p3Re872cK4ERERCR7mMGiP8ARM2gLgxz+6FqiEbbYdXNJTNzbkQI4ERERyR6RMPzi47DzVcCDlnpwET+/W6LgLDZ1SHSULgWmVPujFqqIiIhI8nke3H8G7Hq187Wuqip0TB3iXNbWQhURERHpf021sGN953bL6b6qQmy91BSZUlUAJyIiItkhfwTkFfrHuQUwds4Hr6qQaEo1CTSFKiIiItlhfx20NPrHkWa4+Nd+SpEPUlUhRaoxaAROREREskN09MyCtCFFo9qnRj+I2CnVJFEAJyIiItkhumvU8IOvFErM+0EpgBMREZHs0FTrpwvxIv4GhMbqpKcDOVgK4ERERCQ75I+AAQX+cW4+/P7SlKuw0FsK4ERERCQ7xG5iaGmE7clPB3KwFMCJiIhIdigs9VOGRFOHjC9LejqQg6U0IiIiIpIdElVVSHI6kIOlETgRERHJHimQAuRwUAAnIiIi2cfz/JqmB7OJIQWK2SuAExERkexzsDVNDyXwO4wUwImIiEj2iI6eFZTAuLl+VYZxc3u/iSFFitlrE4OIiIhkh+joWeUaP2iD+KoMvVkXFy3HVblGxexFRERE+lxDNVSWt1diMNqPm2r9zQ09SZFi9grgREREJPN5Hiy/1A/YMH8ELhQ6uJG06E7WJFIAJyIiIpkvWgcVwELwyQf9oE154ERERERSVHTtWrQKQ9HItM4JpxE4ERERyXwpsnbtcFEAJyIiItkhBdauHS6aQhURERFJMwrgRERERNKMAjgRERGRNKMATkREROSDUDF7ERERkTSiYvYiIiIiaSZFitkrgBMRERHprdiEwCpmLyIiIpIGUiQhsAI4ERERkQ8iBRICawpVREREJM0ogBMRERFJMwrgRERERNKMAjgRERGRNKMATkRERCTNKIATERERSTMK4ERERETSjAI4ERERkTSjAE5EREQyj+dBQzU4l+ye9AkFcCIiIpJZPA8ePBvumAoPnOWfZ5g+D+DMLMfMXjazFcH5kWa2xsy2mNlvzSwvaB8YnG8Jrk+KecZ1Qfvfzez0mPYzgrYtZvbNvv4uIiIikgaaamFbOXhh/7Wp1m/PoFG5/hiBuwbYFHP+A+BO59xRwB7g8qD9cmBP0H5ncB9mNg24GDgWOAP4aRAU5gD/BSwEpgGXBPeKiIhINisYAXmF/nFeoX+eYaNyfRrAmdk44CzgF8G5AacAy4NbHgTOC47PDc4Jri8I7j8XeMQ51+ycewfYAswNfrY45/7hnGsBHgnuFRERkWzWVAetTf5xa5N/3lQLlWv8UbnKNe2jcmmqr0fgfgR8A4iGuSOA951z4eB8OzA2OB4LVAIE1/cG97e1d3hPV+2dmNmVZlZhZhU1NTWH+p1EREQklRWWwri5EMqB8fP888JS/ziU296WxnL76sFmdjZQ7ZxbZ2Yf66vP6Q3n3FJgKcCcOXPSf+JbREREuhZd4+aCY+cgFILFK/yRt8JSMEtqFw9VnwVwwEeAfzGzM4FBwGDgLmComeUGo2zjgB3B/TuA8cB2M8sFhgB1Me1Rse/pql1ERESyVVMtbF8LLuK/NtVC0Ug/iCsamezeHRZ9NoXqnLvOOTfOOTcJfxPCn51znwaeAy4IblsM/CE4fjw4J7j+Z+ecC9ovDnapHglMBtYCLwGTg12tecFnPN5X30dERETSRIZNlybSlyNwXfl34BEz+w/gZeC+oP0+4JdmtgXYjR+Q4ZzbYGa/AzYCYeALzrkIgJl9EXgGyAGWOec29Os3ERERkdRjllHTpYmYy4BcKB/EnDlzXEVFRbK7ISIiIn3J8zIigDOzdc65OR3bVYlBREREMkuG5XxLRAGciIiIZJbYnG/byqExSCGmSgwiIiIiKSqaBw7AebD8UoiEM2pUTgGciIiIZBYzuOB+P5Evzh+Nq31TlRhEREREUlrxKBhf1p5KpPSYztUZ0lgy0oiIiIiI9K2OqUQSVWdI492pGoETERGRzBStvGCWuDpDGlMAJyIiIpkvfwSMmakpVBEREZG0EM0Lt/2l9nNNoYqIiIikoGjet8Yaf+dp1I70n0LVCJyIiIhkHs/z871VrvF3n46bC5WrAfN3p2oKVURERCTFNFbDthf948rV8JWNQV44a9/YkMY0hSoiIiIZqEOA9rvFUDjSzw+X5sEbKIATERGRTON5fgmtAYXtbTsq/PVwGUJTqCIiIpI5ojtOt5X7Od/apPeu0440AiciIiKZo6nW37gQF7wB4+f7a98yhAI4ERERyRyFpX6iXsuJaQzBhff76USiJbXSnAI4ERERyRzRGqjXboKJH/GL2U8og/++HO6Y6qcW8bxk9/KQ9WoNnJnlAxOcc3/v4/6IiIiIHJpQyN9tGi1m7xzcOQ28sD+92lSb9tOpPY7Amdk5wCvA08H5h83s8b7umIiIiMghiRazLxrpT6uGcjOiDir0bgTuZmAu8L8AzrlXzOzIPuyTiIiIyOHhef6I2+InoKnOD94yYDdqb9bAtTrn9nZoy4wVgCIiIpK5oilF7pgKD5ydEWvfonoTwG0ws08BOWY22cx+DLzYx/0SEREROTTRlCJe2C+rdedUuP/MjAjkehPAfQk4FmgGfgPsBf6tLzslIiIictA8D+p3+a/j59EW7jjPD+Qa078iQ49r4JxzTcD1wY+IiIhI6vI8P1VItJD9uFX7G/gAACAASURBVDK48q+w9CT8FWBGpzqpaag3u1BXmtnQmPNhZvZM33ZLRERE5CA01fpltKK2l0NRCUyYD6EcmHhC2qcQgd7tQi1xzr0fPXHO7TGz9P/mIiIiknkKS2HsLL94PQAGFoIlT/rBXRbtQvXMbEL0xMwmol2oIiIikorM4LJnYdzxGTXi1lFvRuCuB14ws7/gTxqfBFzZp70SEREROVg5OX4QFx1x8yJw/xnw3sv+pobFK/wkv2msN5sYnjazWUBZ0PRvzrnavu2WiIiIyCGIVmGIhOEXC2DnK357ppfSMrNjgtdZwATgveBnQtAmIiIikro8zx95iwZvAGNmZnwprWvxp0p/mOCaA07pkx6JiIiIHIpo+SznYMf6+GuhAX57mm9k6DKAc85daWYh4Abn3N/6sU8iIiIiBydaPqtyDYyb669521YOBNUXtq/N7ClUAOecB/ykn/oiIiIicmhiy2dtXwsX3g/Xbgx2peb6AV0GTKH2ZgvGKjM73yzNxxpFREQk8xWW+kFabLD2+0thewUc8WFY9ETaT59C79KIXIW/Hi5sZgfwU4k459zgPu2ZiIiIyAdlBoseh9o3ofQYvyZq5Wr/2o4KaKiCIWOS28fDoDdpRIr7oyMiIiIih8zz4KF/8adRx8+D074Xf33/nowI4LpLIzLZzP5gZm+Y2W/MbGx/dkxERETkA4tdA7etHJ78Svu1vGIYOTV5fTuMulsDtwxYAZwPvAz8uF96JCIiInKwYtfAjZ0Fu14NLoTgixVpX4Ehqrsp1GLn3L3B8f8zs/Xd3CsiIiKSfLFr4EqOhofOaZ9OLR6V7N4dNt0FcIPMbCb+pgWA/Nhz55wCOhEREUktHdfALXoc9u/2R+YyYPdpVHcB3E7gjpjzXTHnqsQgIiIiqSd2DVzlGj94S/OkvYl0V4nhn/uzIyIiIiKHLLoGLjoClwFJexPpTR44ERERkfRgBotX+CNxGTZtGksBnIiIiGSWUKh92tTzoLEaML8tQwI6BXAiIiKSmTwPHjgLtr0IGEyYD0uezIhUIt0l8j3dzC5I0H6BmZ3a04PNbJCZrTWzV81sg5ndErQfaWZrzGyLmf3WzPKC9oHB+Zbg+qSYZ10XtP/dzE6PaT8jaNtiZt/8YF9dREREMlp0QwMADrav8dsyQHch6I3AXxK0/y9way+e3Qyc4pybAXwYOMPMyoAfAHc6544C9gCXB/dfDuwJ2u8M7sPMpgEXA8cCZwA/NbMcM8sB/gtYCEwDLgnuFREREWnf0ACAwfiyjNnU0N0U6kDnXE3HRudcrZkV9vRg55wDGoLTAcFPNP3Ip4L2B4GbgXuAc4NjgOXAT8zMgvZHnHPNwDtmtgWYG9y3xTn3DwAzeyS4d2NPfRMREZEsYOZPmWbgGrjuRuAGm1mnAM/MBgD5vXl4MFL2ClANrATeBt53zoWDW7YD0RqrY4FKgOD6XmBEbHuH93TVnqgfV5pZhZlV1NR0iklFREQkU4VCUDzar8KQIcEbdB/APQrcGzvaZmZFwM+Caz1yzkWccx8GxuGPmh1zCH09aM65pc65Oc65OaWlmTF0KiIiItmruwDuBqAK2Gpm68xsHfAOUBNc6zXn3PvAc8B8YGjMyN44YEdwvAMYDxBcHwLUxbZ3eE9X7SIiIpKtPA8aqsG5ZPekT3UXwP3ROfdN/CBpSfAzwTn3Tedca08PNrNSMxsaHOcDpwKb8AO56O7WxcAfguPHg3OC638O1tE9Dlwc7FI9EpgMrAVeAiYHu1rz8Dc6PN6rby0iIiKZJ5o25I5j/FfPS3aP+kx3mxhKAZxz+4HXD+LZRwAPBrtFQ8DvnHMrzGwj8IiZ/QfwMnBfcP99wC+DTQq78QMynHMbzOx3+JsTwsAXnHMRADP7IvAMkAMsc85tOIh+ioiISCZoqIZtqwEHW1/0zwePTnav+kR3AdwQM/tEVxedc92ug3POvQbMTND+D9p3kca2HwAu7OJZ/wn8Z4L2PwJ/7K4fIiIikiXM8BNe4L9m0KaFjroN4ICzgUTf3tHLjQwiIiIi/aKgBPKKoaXefy0oSXaP+kx3AdxW59xl/dYTERERkUOxvw5am/zj1ib/PFoTNcN0t4khc8cdRUREJPMUlsKEMgjl+q8ZUnUhke5G4D7bscHMSoC6YHeoiIiISOowg8Ur/HqnhaUZvQauuxG4IjP7XzN71MxmmtkbwBtAlZmd0U/9ExEREem9UCijSmZ1pbsRuJ8A38LfzPBnYKFzrtzMjgEeBp7uh/6JiIiISAfdjcDlOueedc79HtjlnCsHcM5t7p+uiYiIiEgi3QVwsemL93e4pjVwIiIiIknS3RTqDDPbh78bNT84Jjgf1Oc9ExEREZGEugzgnHM5/dkRERERkUPmeVm/C1VEREQkfWRRMXsFcCIiIpIZosXsvUh7MfsMpQBOREREMoNzZEsxewVwIiIikv48D5Zf2n6e4cXsFcCJiIhI+muqhe1r289bG/1i9hlKAZyIiIikv8JSGD8vODGYMD9ri9mLiIiIpAczWPIkNFYDlvH1UBXAiYiISGYIhaB4dLJ70S80hSoiIiKSZhTAiYiIiKQZBXAiIiIiaUYBnIiIiEiaUQAnIiIimcHz/PJZzvV8b5pTACciIiLpz/PgwbPhjqkZX8geFMCJiIhIJmiqhco14IX916baZPeoTymAExERkfSXPwLGzIRQrl+RIYOrMIACOBEREUl3ngcPnQM71vtB3KInMroKAyiAExERkXTXVAvbysFFYPu6jJ8+BQVwIiIiku4KRsCAguDEg98v0SYGERERkZTWVActDe3n217004lkMAVwIiIikt4KRkBufofGzM4FpwBORERE0ltDDYSb4tsss0OczP52IiIikvk6Vl4YOweKRianL/1EAZyIiIikt1CHcOaiXyuNiIiIiEhKKxoJE04Ay/Ffi0clu0d9LjfZHRARERE5JGaw5Ek//1thacaPvoECOBEREckEoVDGr3uLpSlUERERkTSjAE5EREQkzSiAExEREUkzCuBEREQk/XmeXz6rY064DKUATkRERNKb58GDZ8MdU+GBszK+kD0ogBMREZF011QLlWvAC/uvTbXJ7lGfUwAnIiIi6a2wFMbPg1Cu/1pYmuwe9TnlgRMREZH0ZgaLVyiRr4iIiEhaUSLfw8PMxpvZc2a20cw2mNk1QftwM1tpZm8Fr8OCdjOzu81si5m9ZmazYp61OLj/LTNbHNM+28xeD95zt1kWhNwiIiKS9fpyDVwY+KpzbhpQBnzBzKYB3wRWOecmA6uCc4CFwOTg50rgHvADPuAmYB4wF7gpGvQF91wR874z+vD7iIiIiKSEPgvgnHM7nXPrg+N6YBMwFjgXeDC47UHgvOD4XOAh5ysHhprZEcDpwErn3G7n3B5gJXBGcG2wc67cOeeAh2KeJSIiIpKx+mUXqplNAmYCa4BRzrmdwaVdwKjgeCxQGfO27UFbd+3bE7Qn+vwrzazCzCpqamoO6buIiIiIJFufB3BmVgT8N/Bvzrl9sdeCkbM+T5nsnFvqnJvjnJtTWpr5W4tFRESyQiQMVRv91yyqwgB9vAvVzAbgB2+/ds49GjRXmdkRzrmdwTRoddC+Axgf8/ZxQdsO4GMd2v83aB+X4H4RkYzkeY66xhZKivLQni3JepEw3HYkNO8DywEDxpf56URCmZ/mti93oRpwH7DJOXdHzKXHgehO0sXAH2LaFwW7UcuAvcFU6zPAaWY2LNi8cBrwTHBtn5mVBZ+1KOZZIiIZxfMcFy9dTdl3/8RFP1+N52XPSINIQlUb/eANwEXAi8DWF/2RuCzQlyNwHwE+C7xuZq8Ebd8Cvg/8zswuB7YCnwyu/RE4E9gCNAGXAjjndpvZd4CXgvtudc7tDo6vBh4A8oGngh8RkYxT09DM2nf3ALD23T3UNDQzavCgJPdKJEk8D564JsEFlzXTqH0WwDnnXsAf0ExkQYL7HfCFLp61DFiWoL0COO4QuikikhY6/p+pJlAlq+17D3auT3YvkirzJ4lFRDLAiMI8igf6/+YuHpTLiMK8JPdIJEnCLfCjY7u+ngXr30ABnIhIWtjd1EpTSxiApuYwu5tak9wjkSTZVt71tbFzsqaclgI4EZE0UFKUx5xJw8kNGXMmDaekSCNwkqVGTO762oUPZUUhe1AxexGRtGBmPHxFmdKIiAwe7acLqUwwErd8MVz2bFZMo2b+NxQRyRChkFFaPFDBm2Q3LwKt+xNfe+9laKrt3/4kiQI4ERERSQ+eB8tOh12vtrflFsDY4yGUC+PnQWF2VFzSFKqISJpQJQbJeg3VsKMivi3SDBf/CizkB29Z8t+GAjgRkTTgV2IoZ922PcyeOIxHrigjFMqOX1QibToGZ5YDE8qgaFTWBG5RmkIVEUkDNfXNrH13NxHPsfad3dTUNye7SyL9r2gkTDgBCMGAIr/qgudlTfWFWArgRETSQMfBBee85HREJJnMYPETsOgP0NoAeFC5Ghqzo/5pLAVwIiJpYERhHtPHFredn/qj5zlwIMzmnfvwPAVzkiU8Dx76F3jo3JhGIxuLy2kNnIhIiguHPS5cuprXdtS3tdUfCDPru3+iqSVC8aBcXr7hVHJz9W9yyXBNtVC5Bgj+0dK2Bi47qi/E0n/tIiIpzPMcn1y6mpe3vR/XXpAXoqklAvjB3JaahmR0T6R/5Y+AMTMhlOOvhbt2Eyx5Mus2MIACOBGRlFbX2ML6DsHbE1efwGs3nkrxoPbi9lNGFSWjeyL9x/PgwbNh+0t+Ml/nsiptSEeaQhURSWElRXkcM7qIzbvaR9j2NYfZsz/C+us/ztu1jUwZVUQoC0oHSZZrmz4NVJb7bVk4fQoagRMRSWlmxhNf+AjRlG8h4K5VbzH/e6v4zLK1TBlVrOBNskNhKYyZ035uIcgflrz+JJn+qxcRSXE5OTnMGj8EAz40tph1294n7Dkq3t3Nm1X1uCzMgSVZyItAS/tGHlwEat9KXn+STAGciEiKq2ts4ZXt+3DAG+/VM33cEHIMCgbmctaPX+DipeV4noI4yWCeB/edBjUb29vyiqD0mOT1KckUwImIpLiSojxmTxxGbsiYM2k4y6+azxNfOpHG5jCRYCSurrEl2d0U6TsN1fDeuvi2z5dDFi8f0CYGEZEUZ2Y8fEVZWyF75+DG/3md6KBbQV4uwwsGJLeTIn0p0U7T5n39348Ukr2hq4hIGgmFjNLigZgZVfsOULFtb9u1ptYIu5tak9g7kT5WNBLGzms/HzgYRk5NXn9SgAI4EZE0Eg57XP7A2ri26WOHUFKUl6QeifQD5yAnBzA44sPw9XeyevoUFMCJiKQNz3Nc+PMX2bgrvurCzz4zC8vSZKaSJZpqYftawEHVG3Bgd7J7lHQK4ERE0kRdYwuvVu6Na5sxdjBmplQiktkKS2H8PAjl+q+FpcnuUdJpE4OISJooKcpj5vghrAuCuEG5YCFj/vf/zOyJw3jkijJCIY3ESQYyg0WPQ+2bfuoQjThrBE5EJF1EIo7NVe3TpwfC8ErlXiKeY+07u6mpb05i70T6UCQM958BPz/Rr4fqecnuUdJpBE5EJMV5nqOusYXa+gM0tkS6vE+DEpKRImFY+s9Q9Zp/vvVFPy/c4NHJ7VeSKYATEUlhnue45N5y1m3dw8zxQyjKy6EhCOJij+dMGEJp8cBkdlXk8PM8WHZ6e/AGgNO/VtAUqohISqtrbGHd1j2EPcfLlXt5+KqytsL2DS0RCvJyAPh7VSORiDYySIZpqoUdFfFtAwqhoCQ5/UkhCuBERFJYtIxWjsGHxg7m1sc3EFv2tCkYgatvDrOlpqGLp4ikqfwRUNIhYW/rfthfl5z+pBAFcCIiKczM+PXl85g+fiivbt/LS1vfT3hfCDiqtLB/OyfSlzzP37BQuym+fexspRFBAZyISMrbs7+V17fvjRt568gD6lROSzJJUy1sezG+beRxcPmzWgOHAjgRkZQXnUbNDRlzJw3jqS+fSKJfX/qVllye56ipb1ZS5cOlsBRGTIlvO/kbWV9CK0q7UEVEUpyZ8fAVZdQ1trTVPJ175HAq3t1NQV4ujS1hZowfqnqoSRBN8TK8YACf+sUa1m3dw+yJw3hYSZW75nn+6FphafcjaWZw1d/guzHTpcec1ff9SxMK4EREUlw0SCgpymureRoN6IYOyuWT95bz2va9XHLvGgUO/Sia4qXi3d1MGzOYjTvriXiOiq17qGtsUVqXRKLr2irX+CWxFq/ofkQtLw+ur4K3VsKUhZCrsCVK45AiIiksGiTM/94qLl5ajhcshAuFjNLigbx/IMzr2/1qDOuCwEH6R11jCxXv7ibi4PUd+9r+bvIHhBheMCDJvUtRTbWwrRy8sP/aVNvzewYMgmnnKHjrQAGciEgKi80DlyhAi10fN3viME2j9qOSojymjxvSdh5d+dbQHKE2+Hs6cCDMU6/tJBwOJ6GHKcbzINwMucHIZG4+eBHobs2g5/lVF7SusBOFsyIiKSwaoEXXVnUM0KJpRt6srmdEoYK3/mRm/PRTs5j/g+c6X8MP3o65+Zm2ts03n86gQVn6azfc4pfDqn6jva21Ae481p9KXfJk56nUaP3T99bD+LKep1uzTJb+L0lEJD1EA7QtNQ1MGVXUtgYuyvMcn/pFOWvf3QPAh8cN5r8//xFyckJx93RcQyeHzvMc1/z2lU7thXk5jCjM49kNVXHtf/57NWfOGNNf3UsdkTB8dwJ4+ztfcxE/VUj9LhgS82fjefCLU2Hnev9c9U87USgrIpLC/ABtDWf9+AUuvndN2zqrqLrGlrbgDeCV7fs4/54X8TyH5zmq9h3g4qWrKfveKi6KWUMnh66usYX12zonVt7fGmF3UyunTotPNrvsxXey88+/amPi4C1WQ038+b732oM3AJymUTtQACciksJq6ptZ++5uIp5j7Tu7qalvjrs+vGAA+bnxo2qvbN9LVf0Bf/PDd1ex9t09Xb5fDl50ervjmKYBQwflEgrlcNzo9uoYFVvfp2rfgX7tY9JFwnDf6T3fFzsy7Hnw64v6rk8ZQgGciEgK8zwv7rzjDGhtYwv7w51HJt4Pdkh6HdpbI5HD3MPsZWb88tK5TBlZENcecfDS1t3U1B/gjV2Ncdfeb8qyXcJVGyHS1PN9+UPhH3+FvTuhsQZq3uh8j9a/xdEaOBGRFBUOe5z6o7+2nR8/cWin3GKJVrQVD8zlyOEF5OUa+1vjg7utdY2MG66aqYeD5zk+uXQ1f6/uHKB86hdrKczLiWvLzzUmj9SfPSOOgX2V0BoEt6Nnwl0zIPrPjcKxnd8zfj4Ujey3LqYDhbMiIilqS00DDc3tI2a3nntcp00IIwrzKOgwhdrYEub8pas7BW8Ad63akp3rsPpATX0zr2zf2+X1xpb40c79YcfM76yiqamV1VtqiWTDaGjHoGtAAVz9InxzG1z1PIyZA7tehtix4sYd8e/5zONw2VOqf9qBRuBERFLUUaWF5Jg/JZdjMHlkUad7dje10tRhCnXKqCI2vFef8Jnrt72vKgGHycHEE/XNYY77zrNEY+jHPz+f48YPJdTX04OeB43VgPlBVX8FQ8Wj/NGzytUwegZc8RzkBCOTRSPhvYqenzFxvoK3BPrsfzFmtszMqs3sjZi24Wa20szeCl6HBe1mZneb2RYze83MZsW8Z3Fw/1tmtjimfbaZvR68527T3ngRyTB79ofbftF7zj/vqKQoj+MnDm07L8gL8fjVJ1CQF/9/7yGDHCX7PaxGFOZRMKD9V0/HzSRdiR0A/Zd7VjP1hqdobm493N2L+UAP7j8Tfng0/HAK/OLjcKijf71NsGsGl/4RvvYWXPWX9uANer+rdPeWg+9nBuvLkP8B4IwObd8EVjnnJgOrgnOAhcDk4OdK4B7wAz7gJmAeMBe4KRr0BfdcEfO+jp8lIpLWhhcMoChI/Fo0KDdheSYz47dXncDq607hqS+fyBs3n87eZo/9LfHbFzwHD112PI9cWaZccIfJ7qZWDsRMUyfaTNIbzR4cfdOztLT00ZRqQ7U/Aha1owKWne4HYQcjWs/0jqnwwFk9PycUOvhRv4GDofSYg+tnhuuzAM4591dgd4fmc4EHg+MHgfNi2h9yvnJgqJkdAZwOrHTO7XbO7QFWAmcE1wY758qdcw54KOZZIiIZYXdTK03N/qhbU3OY3U2JR2lCIeOIIflMHlnMm1UNDB2U0xb4xTqqtHMi4I7CYY/NO/d12v0qnQ0vGMD08e2ltI6fNIzpY4sP+nmr3+lFXdCDkWik6711vatDGhUJB/ncPP99lWv8eqZbV0P1pq5H07obqesu3j3rR3DF8/DvW7X7tAv9vQZulHNuZ3C8CxgVHI8FKmPu2x60dde+PUF7QmZ2Jf7IHhMmTDiE7ouI9J+SojxmTRjGum17mNXD1Gc47DHzP1ZSfyBM4cAcmprjR3OOnziUkYMHdft5sc8oHpTLyzecSm6ufnkmEk2w/Pr2vcwcP4SffWY2IwcPYtf7+xOW1uqNnL76o44kSF2SOwjyh/fy/WH4wSRoqYfQILj4YRg7ByrLAQ9+doJf6urCB/01b9F/JHieP0JXuSZxuaxEgZnlwIQymLNE6956kLT/MoORs37ZCuWcW+qcm+Ocm1NaWtrzG0REUkBrq8fGnfvwHGzeWU8k0vX/ZW6paaD+gD9a19gcYdCA+P97b26NsKmHkbU3q+vbnlF/IMyWmobD8C0yU11jCxVb9xBx8NqOfYRCIZyDLz38cqd7jx1TzPNfP7nHZ9696u24HcItLZFD360aboGfzO3c3toEy07r3TRqzWY/eAPwDsBv/jUI3mJUlvtTqvef6Qd8DdVQv9MvkxVbLitW0Uh/g0PU2Hlw7SY/0FPw1qP+HoGrMrMjnHM7g2nQ6qB9BzA+5r5xQdsO4GMd2v83aB+X4H4RkYzgeY7zf/5iWyqK+mY/oDrmiMEJ758yqojiQbldjsC99l49Z979AoV5Obzy7VMZMCA+R1k47PHvy+Preh45vPsRu2w2vGAABQNyqG8OUzAgh+EFA6ipb6YiQWmt/Nwcxg4r4n8+P5/z7mlfi2bA/3xhPv/609V4Ln6HcEtLhKk3PU00Zn/+qyczrqTnKfA4ngdLT/GDrkR2rPenQ3vKr1ZQ0tsP9AO1ZafDjnUw7J/iLzfUxNc7jW5wSMbu2AzQ3yNwjwPRnaSLgT/EtC8KdqOWAXuDqdZngNPMbFiweeE04Jng2j4zKwt2ny6KeZaISNqra2xhw459beeFA3OYMqpzGpGoUCjEyzecyh+/fCKrvnIyxx85PGGS38aWCP/6sxfjRno8z3HBz17k9ffiR9ym3ryy7xbWp7ma+mbqg/WJ9c1hauqbu4w91lf6gVleh+noY8cM5kNjh3L8pOHkdtghvG7bnrbgLUSYy3/4az750799sBx++96D6te7vj6gwB8t62k3aGEp5BR0f0+sHRWAgz0dd48m+AMKhaB4dPzUq/RKX6YReRhYDRxtZtvN7HLg+8CpZvYW8PHgHOCPwD+ALcC9wNUAzrndwHeAl4KfW4M2gnt+EbznbeCpvvouIiL9raQoj+OPHE4I+NDYwbx246k95goLhYxbntjIR37wHK3hCKu/+c8cP2lYp/s27NhHTX37qExXCWk95wcS0tmeDiWx9jT5I2dzE/x5zwkCs2OOGExRUJ2hIC/EH75wAqFQiIevKGP1dQvidgjPCjZHhAjzysCreGbgv3Nf9QXU1feiLBX4gdmWVZ3bz1tKWyDV2gh3ToVlC7ufSq3f2btyWD0pViWFw6nPplCdc5d0cWlBgnsd8IUunrMMWJagvQI47lD6KCKSqsyMX18+jy01DUwZVdSrRK91jS289O5uPAcvV+7lc79cx2P/9wTqmlo45f8915bw1wGn/PAvbVOp3Q18zByXeMo2G4XDHm9W1zOiMI9Jw/Ljrv1TSSFmxiNXzqeusYXBeTlUbNvDUSMLGTk4HzPDzHjlxtM6/Z2GQtYpsfI7u/cDcBQ7KWY/ZlDMfoob3oYh07vvaOsBuO1If51bR0ee7Jeu2rW+va1ytT+NWTw68bN+dGzPfzg9GfZ//FE2OWy0vUhEJAV5nuPT963h7B+/wCX3runV1NnwggHkx9TffGPHPi5cuhrDOuUoa2yJ8K/3+FNyQwZ2/W/5C3v52ZkuHPaYccsznHn3C8z73p+Z9d0/xV3/R50fLEWDsYEDc/nI5FJGDSmIW7eWmxvimCMGxwXknueoqW/GOdeWxuX/jMhnUC5sYRSO9llOu/dkaG3uuqORMHx/UuLgbcJ8KBweH7y1SRDFex78/GNdfxbAyOPg2s1weYLRvlifX6Mp0sNMpbRERFJQXWML67buIew51m3d06vyV7ubWtnfYfPCK5V7iTiPwoE5cXVVAd54r56ahmb+Ud31btM3duyjtqG5xxQkmW5z1T4aW9unGWPrzBbmdb8+sTue57h46WrWbd3DzPFD2LyrgYaWCCHzg7bTS/ZhDbHhlYNfnOLXEU00Klv7JkT2J/ikkJ/mo/KlzpdGz0i8kaGpFmo3df8FPr0cBh/hj95FS2bZAHCtUDwJTvwKzPo0DOichFoOjQI4EZEU5OeAG8q6re8za8LQXpW/KinKY/bEoby0NX4n5N6mVva3Jl7j5Jxj1vghGO15nSaPyOOtOn+NlwM+/8sKfv/5jxAK9ZwE+I2d7/PengOcNm0kubmZ8SsmHPbYVtvY5fVV15580LVMaxqaWfuuv86wYlv7OsTooOfK3SNweQMxYkbdqjZ2vXu09BjIye8cxB3xIf/+RDtKz/lJ4tGxwtL2oKwjy/Fzuw0Opl2jO0qbaqFgBDTV+e/XqFuf0RSqiEgKam31eGPHXiLOsfG9fd3mgItyDlo73Ddz/FCOHl3M7AlDHKiCHQAAHnFJREFUE74n7HnM/I8/tQVv08cUcc3p0+LuWVe5l117E43qxDwn7PGhm5/ivP9azdW/eZmjbniGAwc6125NN9Hkxlc//ErC60UDcygtPvjRSdfDDlDnjMjlT8c35g70g6REvDBc+ACdfr0v+I4fTOXkwLeqIBQzmvvMvyfexBANyq7dBFc+D2PmBIHbfL/t0j/GB2jRklmhHKUE6QcK4EREUoznOc776Qs0BaNmDS0R3qyu7/F9NQ2dd5M+fPlcQqEQd188s9P9U0cVcuWDL7V9DsCGnY1MGt45ZcTbNd1//paaBvZ3iNeee7Omxz6nutjkxok0NEeobUhQ6aCXegpxPAdv2ZGQG/N3Et4PDVWdbw63wH+UwiMXAbEBWQiOPLH9NG8QXPNK+6dvWw2NXfxdhUIweAyMmQ6fWwlf3QyXPaW0HylAAZyISIp5b3cTG3fFr0sbUdjzFGqiX6cvJ0gPErWpqpGNu+KnBqceUcy0MUM4bnRhXPv3n9zAb8rfZeeehoSjRkeVFnZqO3Vaele+8Tz3/9u78zA5qzLv49+7lk7vS5JOpzs7CVkgkISErIBoJMZlYHTgHZh5AXEJLjg46qW+KiAKjsu4zSAoLy64AY6g4ga4REBDgARCyGYgJOns3Vk6vVZXV9WZP57qLV29ppauzu9zXW1XPVX19HkOZdWd+5xzH275xeZ+n9dfFq0vAynMO6YoF244JQt3dFfP+m3Vp+yOALDqP+CWo17mravCcjrfMQ7+5/rEWbhIGHb/FaLR09uUXpJOAZyIyDCz+UD3oGv62PwBLSIoLxrFwi6bq/sNFk/z6pL1N3+t3X3XLcTn8/GrD13C7IrOrM/WIy186pdbWfalJ7ny7nU9VqbW1HdfGTmzPJ/+80vD27GmMC/uq+/3eQPt20TKi0axaHJJr4/Pn1js/bcff643fNnuh2+Du5dDuLVzs/hTdz4wHyy5sWfwBnD0Fbpl6fY913Nz+7YQ3FkB97/Vy+xFhp5plORTACciMkzEYo4Ddc3kBrsHR7/70IoBZWrMjIduXM6jNy3nJ+9ZzM47VuOPf3mXF41iwaSegUJeoPO8i6eWUVHi1Tfz+318950J9tAENu6ro7axM2CLRGKs/Ppfuj1nZ20zc297jEgk1vGcbQdPcuRky2llrNKpfSFJX+ZNLO53dXBfzIyfvW8F6z75euZWFeMzKMjp/GrOCfi9RFvL8Z4vrt0GXxgHX53tbZm195TFBjdvTRy8gbfYIaeo8/6kJd6ig1jM28Hh4Etwz0Xg4kGei8KedUO+Tkm+kbFESEQky8Vijqvu+SsbE2R86sOO8gHECO214zbuPcHCKWUse+/SjsfMjJ+/bzlXfucZNlXXMWd8AT6/n60H6plbVcR91y2ioiSvW6B4vKn3jEvXcHLH4fpuZTXahaKw7fBJZo8rZv7nn+iYa7d4ahkPrll2WpmrdPDizO7XlR80mrtc673XLhrc/qQJ+HxGVWk+j950EceawkQiUZZ9aS0Az+05QW1DKxXF5VA5Hw5uTNDQKBx6AX757s5jOUV9F871+eATe7yN6vPHes91ztuVYX+CoVjofeGEZIQycCIiw0BNfShh8LZggCVEIHHtuK78fh8Pv2856z+1kpxAgC0H6nF49eDe95MXekyp8vdSGmPx1LJuWaeTLb0Heidb2nj7PX/ttlBiw56ebRuOjjWF2VjdfTj7ZzcuY8GkUvw+Y/G00Umtj9deBNjv797vZvH/ectXB36ycBO0HOv7Of4AjJ/rlQIx83Zj6C14C+R7w7gybCgDJyIyDBxr6lldf3ZFAQ+/b9mAMzxeHbiyjgxcosDP5zOikViP1aqb9p2ktrGVii4ByezxReQHfd2Cr7uumc+8ScU8s+soS6aNxjmjNC9xkVYfcHZ5AVsPdV+QkZvjZ3R+kFjMcawpzNjCHKJRN6htw9JhdH6Q/BwfTWHv+otGBTinqoSH37+8o92nm31LpH1P1fb/jh3Bsq+X4dBEKs/zhkQHKhKG6ud6f/xdjycuHCwZowBORGQYGFvYc4y0MDfIYBYCmBkPvHdpv8HFa8cS77wQi3Zfhejz+Xjp1lW8/Z6/seVgAwbcdEo9tPwcP83hKHl++PL/mc+FU0opGZXD27/zDDuPNLDmhxt6/J3WthhHm8J88Mcb2VBdx4QiH0dboDUSoyg3wIufuQyfz1IaJA3E8eY2QvEtyAz4Y5eCvacz760/XfdU7Xb9iQr39ubqhwa+WjQShjsq6F565BQFCQoAS0YpnBYRGQbGFecyb0L3jeM37K2jtqGPfS+HaEZ54m2f6lraehwLBv08etPF/OTdi0m09KA57G3P1RKFDz+4iZsf2kx9OMqOww3EHGw+2DNYXDCpmHBbhA3V3o4RBxpitMYXOzSEInz19y+z+ptPcuGdf+T1X1lLa2tmhlvHFuawcHIZfp9xYZKHS/vTPpzaLXgtqvCK6OKDiUvh5pch2L5SOADlc7yVqpOXd+6QMBDV6+kzeIPBZf8kLZSBExEZBqJRlzDZNpjkUyzmuOb/r+8YenvgvUsTLhSoKMljwYRCXjzQGVwV5viZNb6ox3PBCyZmjOt/r8+ogw17T9Aa7XsHhg3VJ7nirqd6ffyev+3vuL3neAuzbvsD225dRX5+evfTdM6r8db5k+ESaF23qyoo94rvRuPBrQ+47pde6ZDBbmFVMrnvxycvH1z2T9JCAZyISIaFw1Eu/9bT7DjSWVTXgEVTSgc1VJdoEUOi15sZD3/wEg7Vt7CrpoGxhaOYPb64z7ln44pzmVORz/YjzX22Yc74Irbu77922vGWwZUS+dXmg1yzdMqgXnO6ahtaeX6vt0/p8+2rQUvSl4VLqL2YLsT3Kl0C+571fhcOYXeESBj+e37P41f/HKrmegGhivcOSwrgREQyKByOMu9zj9MS6Qxo2jeW3364gWjUEQgkbxFDO5/PmFCaz4TSnttmJWJmfO+dSzrKW/Tmk6tnsnjq6AGdczAunZX+EhaRSLTb/WEXw5jB9b/pzMgNpYF713NqqRQAqs4b3DCspJ0COBGRDAmHo6z6+tpuwRt0fp02tkZ5tbaR2ZXFPV+cwEAXMQzV+NI85k0o4qUD3fdFbQ84fcCy6WM53txzCPW8ygL8PmPTgZ5z4uZNKOalA17WbkwuHAt1PuYzWDiljPGlPbfqSqVwOMpFX/lLx/1FkweXDU2brhm5wYrF4M939DweLIxvtSXDmQI4EZEMiERinHf7Y7RGez7WvrKzKDfAzIr+55511T75PRXMjEc+cBH/cNfTbOtSGuTpT1zKvmMtLJ5Wht/vZ2yhj8VTy3hujzf8OH9iMY98YAVg7Ktr4k9bDxFqbWPy2EJWz63C5/NT29iK4a3ubGuLsbH6BIumlHKwIcT6V44RiUQIBtMzBy4SifGzF6q75aU+tHJGxlbDpkxTDRx4tufxaAs0H9O8t2FOAZyISAa8WtuYMHgDeHDNEnL8/mFVE62d3+/j1zddzJXffobNB06ycEoZE0rzmVjWmSFrL4PRNShrD36mjC7kXRef3eO8XevP5eT4WTZjLM3Nbbzuy08C8IlfbEnpQoZIJMartY1UFeWw+ItrCUW6r8r0j7DYzZPoogwmLR1cDTnJCAVwIiIZMLOikFy/t91UVwU5fuZWlQy7wK0rv9/XbzFbn8+6BWVD8euXD/W4/89L+lkxOQThcJQFd/yBpnAvETXe0PCIkz+azgHwuBuf9nZnGGnZxhFo+H5CiIiMALGYo7ahtccG7j6fj5c/u5o5FZ2Zq7lVRWy+7bJhHby1S1inLMnecUFln/eTIRZzXPGtp/oM3u5/50L8vW0Kn82OvkKPBQxacZo1lIETEUmRSCTGVfc+w+b9J1mUoC6bc/CZt53LWeX5+H3+lAdE2SYYDLLt1lX8avNBLp01hkAg+V9ZRxpC/ZZGSbRLxogwdiZeHic+XDx5mea9ZREFcCIiKRCLOa76zjpe3OftOfrs7uPUNoSoKMkDIBSKcM7tjxNz3vyq7bevVvCWQG5ugF++dJBbHt3aZ3HioTrR1PcuD/kBY05VSdL+3rDSctxbxRqLeTs4XHW/sm9ZRAGciEgSxWKO2sZWDp1s7gje2q382lNsuuUyYjHHm/7rKWLx0auog43VJ1g2YwTOszpNx5rCPLf7OA54bvfxXosTD9WYgu618ioKgzy45kL21YUHVOA4q/UoBKzsWzZRACcikiSxmOPqe5/pKJ9xqsbWCNsOn+Squ9fR2mWRo89g8bSyNLUyuxTn+DtmaTkgFArjkljjblxxLosml7Khuo55E4v5xQdW4PP5mHYmxDLJKAQsGaMATkQkSY41hXsN3tp95MFN3YI3gO9dd8HInCSfBBv2de/Pi7/6FIumlPLge5dR29RKXVOY6WML2FnbiM+M2eOLEmbMYjHXbdVsKBTht1sPcrIlxInmVnwGOQE/iUtrjGCnUwhYMkoBnIhIkpTm9v+R+urRnhPmy4syvL/mMDZ9bM8dGDbsreMdd/+VzQcbejxWNCrAi7dcRiDQGcSFQhGuuPuv/L2mifGFftZcOpXP/WZXj9cOm/1ORQZghA7si4ik366jTf0/6RS5fkbuJPkkqCjJ49yKvB7HEwVvAA2tEZ7ZVYNzjkgkxgvVx5j92cf5e4333+ZwYzRh8NZOo4iSLZSBExFJkqllPQMNgNuvmM1tv9rR4/jk0Xms/ejrRu4k+SRwDgKBINAy4Ndc+/2NjMuFE2Foi/X//HbzJxYPz/1ORRLQp4aISBJEIjEuuOMP3Y75fcaSaaN505zxCV/z0HuXaO5bP441hXk5vtH9YNSEBhe8nVdVyCMfWKFSLpI1lIETEUmC7YfraY50r2r/m5tWMLuymFjMkReAlkjnY4smlzC+ND/Nrcw+YwtzWDS1rN/FIQPVXra2qijIQzcuIRjMwWep31VCJNkUwImI9CESibHjSH2fKxwBTjS1drs/a1wBsyuLMTOON4fpulPTbz+0gnOqShQwDICZ8eCaZdQ2tnKkronL717f7fGgwQ/efSHzK4s57/N/oq+k27wJRTz8/hWcaIn0uoerSLZQACci0otIJMbc237fseF8DvDdGxaxfMbYjqHP9nIUX3tse7fX/uCGCzsCBC+LNJqNe0+wcEqZgrdB8vmMiuJcxhWNYsaYPF491jkf7ns3LGLFDK8Mxs473szLB0/w0Z9t5rWjzR3btOf6Ye3HXsf40gLMjPIiDVtL9rNTN1ge6RYtWuQ2bNiQ6WaIyDDWXjNs//FG3n7P+oTP+fgbz2Lx9NFc+Z2enydzKwv59b9d0i1IO7UOmQxNW1uEWbd2bkG2847VPeYRtvd1aW6AXUebmFlRqIUikrXMbKNzblGP4wrgREQ6ebsprOe5PceHfI67r5nPW+ZNSGKrpKtwOMrG6hMsnlamRSAy4vUWwGkIVUSki9qG1tMK3gBWnVuRpNZIIjk5fu0bK2c85ZRFRLo4nVGJALDzc5cRCOjfxiKSWgrgRETiIpEYWw/1Xq5i+phR5AUTf2zOGlfA3+9cTU5OTqqaJyLSQf9MFBHBC97Ov/0xmtt6ZuAumFjEt69bTHnRKKJRxys1Ddz8wEZ21nauhrz1bedoPpaIpI0COBE540UiMX7+4t6Ewdu6j19CZVlhx8rRQMCYU1XCb//tEmbe8jgObyhj6fQx6W20iJzRFMCJyIgUicTYfPAE2w7Uc96EIoKBYEch3vbivKFIhE17j/OF372SsADsD6+/gKrRRQnPHwwG+PvnVms1pIhkhAK4DAqFIvxhxxHOKs9nTmWJ6hSJDEBvJSRCoQhrd9Zy8fQyfr31EJ96ZBun5tMCwDeumcu/P7Sl330yDVgxc1yfz9FqSBHJFAVwGdLYFGbu5zs3vvYDD9+4mPOnjAGMfXVN/GnrIVrDEWaML6SlDSaU5XLkZBurzhmnVW5yRonFHPvqmnhi837ufGxXx/Ef37CAzfvq8fna+OIT1f2eJwLc9MCWfp93/3ULuGhWhbJqIjJsqZBvBoRCEc67/XESTLfBB1QU+TnUEO35YBcvf/oNFBXlJaWgpSrESyLtQdNTO2qYMiaPSWPyWffKcc6pKuRocxtH6kIsmFTKnKrSIWeP24c5X9x7nFAozKhRPlpbY91+h0JRfvL8/n7/P5Es2297A3l5eWn5WyIi/VEh32HCW+mWOHgDiMGAvqjOu/PPfPzSSXzlL/s6hok+9cazaHMxCvKDzCgvZtn0cqJReHpXDbX1rfzTBRMIBoMd7dh04DjP7qzlp89Xc6A+QkW+ce3SyeTm+nt8iSb6nZcXoCg3SEMogt+MSaPzGRUMsnTaGGUuslhjU5gfrtvFj57bO+Cg6aOvn0Je3sDeN7m5fnDQ3NLG19dW97n5eLpUFvj49D+cy+q5Vcpui0hWUAYujcLhKD9Yv4sv/O6VjPx9gJsvnkxBgY8vPLYnpX/nI6+bQn5B31/o7V/kodZor88pKRzFytnjOzah7k+qttiJRGK8WtvIzIpCYjE6bg818xQKRfj1lgPU1DUxKscGFPik43dDQ4hvPn0waf2WSRX5Pr501TzOGlfIu76/gVePtnR7/D//aQ7jSvKZVVHMuOI8ZZ9FZFgasXuhmtlq4Jt408juc859sa/npzqAC4ejPL2rhj1HGwiF2jq+HAMBx388vnfQ5/PBsMhQZFp5Lly/fEqf2cHm5ja+9pd9Ha8ZSBA5kN8tLRH++6lqwvFkVNBHxwT4T686i3B0cOdragrz9Sf3Z6AXR4aq4hwO1oe7Hfv0m84iho/XzxnLkztO8NbzK7qV/ojFHEcaQtTUt9DYGmXptNHKEotIVhiRAZyZ+YGdwGXAfuB54Brn3LbeXpPKAC4cjjLz1scG9Zqtt76BPcdDvPWudd2O33H5bP75wsmY+dlxpJ5ILMbJljaCFuFfvrspmc0WGRb6GobNzfWTH8zhyoWT8PsDCsZE5IwxUufALQZedc69BmBmDwJXAL0GcKn0fPXgNsBunyx9bn4eOz+3mmd2HyXgtx5zyOZOKO32ui23lPOjZ14jmOOIRIz5U0p4YfdJcnJcjyzUQHxm9XR8fsM51+dw5qlZqW+srSY908olEz6zahoF+blcfn4Vz1XX0dQa4fyJxbx2tJmg38f8qhIe236EuuYWQqHIgLOQXYfO8/ICVBTl86ZzKwc196yyJI/KEi00EJEzV7Zn4K4EVjvn3hO/fy2wxDl30ynPWwOsAZg8efLCvXsHP5Q5EIfrmln6xbX9Pi8IvPzZleTm5qakHV2HcdvaYiyfMZai/BzWbj9CS3ylX1sbzJ1YyvIZ44acvWifFzajvIDapjA19S3UNIaoPtrYbfh4qHPgGhtb+cZTB5LcO8NLeZ5x46XTaA0Pjzlwra0xyopyuWrh5I4FLyIikjkjNQM3IM65e4F7wRtCTdXfqSjJ48IppTy/t47KQh//umQSuaM6h4QiEeOis8s5Z0JZSov25uT4WTmnEqjsdnzaxcVJ/TuBgI/Zld45U5URWXPpuTz0QjWxWNuAsoMF+UGqSvPYd7xlQEHkQH+3r7itb2kjHI4xdVw+u480k5PDkM432MUZIiIiXWV7AHcAmNTl/sT4sYwwMx66cblqqiVRfn6QGy6anulmJHZephsgIiJnqmzfu+l54Gwzm2ZmOcDVwKOZbJDPZ5QXjVLwJiIiIimT1Rk451zEzG4CHscrI/I959zWDDdLREREJKWyOoADcM79DvhdptshIiIiki7ZPoQqIiIicsZRACciIiKSZRTAiYiIiGQZBXAiIiIiWUYBnIiIiEiWUQAnIiIikmUUwImIiIhkGQVwIiIiIllGAZyIiIhIllEAJyIiIpJlFMCJiIiIZBlzzmW6DWllZrXA3ky3YwDGAkcz3YhhQP3QSX3hUT90Ul941A+d1BeekdQPU5xz5acePOMCuGxhZhucc4sy3Y5MUz90Ul941A+d1Bce9UMn9YXnTOgHDaGKiIiIZBkFcCIiIiJZRgHc8HVvphswTKgfOqkvPOqHTuoLj/qhk/rCM+L7QXPgRERERLKMMnAiIiIiWUYBnIiIiEiWUQCXJmY2yczWmtk2M9tqZjfHj482sz+Y2Svx32Xx47PN7BkzazWzjyU4n9/MXjSz36T7Wk5HMvvBzPaY2ctmtsnMNmTiek5Hkvui1Mx+bmY7zGy7mS3LxDUNRbL6wcxmxd8L7T/1ZvbhTF3XUCT5PfHv8XNsMbMHzCw3E9c0FEnuh5vjfbA1294PMKS++Fcz2xz/bFxnZvO6nGu1mf3dzF41s09m6pqGIsn98D0zqzGzLZm6nqRwzuknDT9AJXBB/HYRsBM4B/gy8Mn48U8CX4rfHgdcCNwJfCzB+T4C/BT4TaavLVP9AOwBxmb6moZJX9wPvCd+OwcozfT1ZaIfupzTDxzGK4CZ8WtMd18AE4DdQF78/s+Ad2b6+jLQD3OBLUA+EAD+CMzI9PWluC+WA2Xx228Gno3f9gO7gLPinxEvAedk+vrS3Q/x+5cAFwBbMn1dp/OjDFyaOOcOOedeiN9uALbjfchegfflS/z3P8afU+Ocex5oO/VcZjYReCtwXxqanlTJ7Idsl6y+MLMSvA+k78afF3bO1aXlIpIgRe+JlcAu51w27LrSIcl9EQDyzCyAF8AcTHHzkyaJ/TAH74u72TkXAZ4E3pGGS0iaIfTFOufcifjx9cDE+O3FwKvOudecc2Hgwfg5skIS+wHn3FPA8TQ1PWUUwGWAmU0FFgDPAhXOuUPxhw4DFQM4xTeAjwOxVLQvXZLQDw54wsw2mtmalDQyTU6zL6YBtcD3zRtWv8/MClLV1lRKwnui3dXAA0ltXJqdTl845w4A/wlUA4eAk865J1LW2BQ6zffEFuBiMxtjZvnAW4BJKWpqyg2hL94N/D5+ewKwr8tj++PHss5p9sOIoQAuzcysEHgY+LBzrr7rY87L7fZZ18XM3gbUOOc2pq6VqXe6/RB3kXPuArz0+AfN7JLktzT1ktAXAbzhgHuccwuAJryhhKySpPcEZpYDXA78T9IbmSZJ+Jwow8tMTAOqgAIz+78pam7KnG4/OOe2A18CngAeAzYB0dS0NrUG2xdm9nq8wOUTaWtkGqgfOimASyMzC+K98X7inHskfviImVXGH68Eavo5zQrgcjPbg5cCf4OZ/ThFTU6JJPVDe5YB51wN8Au8IYKskqS+2A/sd849G7//c7yALmsk6z0R92bgBefckeS3NPWS1BdvBHY752qdc23AI3hzgrJGEj8nvuucW+icuwQ4gTd3KqsMti/M7Hy8KTZXOOeOxQ8foHv2cWL8WNZIUj+MGArg0sTMDG+O0nbn3Ne6PPQocH389vXAr/o6j3Pu/znnJjrnpuINE/3ZOZc1/7JOVj+YWYGZFbXfBlbhDZdkjSS+Jw4D+8xsVvzQSmBbkpubMsnqhy6uIUuHT5PYF9XAUjPLj59zJd6coayQzPeEmY2L/56MN//tp8ltbWoNti/i1/kIcK1zrmuw+jxwtplNi2epr46fIysksR9Gjr5WOOgneT/ARXip3c14afxNePMxxgB/Al7BWyE1Ov788XiZlXqgLn67+JRzXkr2rUJNSj/graR6Kf6zFfh0pq8tk+8JYD6wIX6uXxJffZUNP0nuhwLgGFCS6esaBn1xO7AD7x82PwJGZfr6MtQPT+P9g+YlYGWmry0NfXEfXqax/bkbupzrLXgZyF3Z9pmZ5H54AG9uaFv8vfLuTF/fUH60lZaIiIhIltEQqoiIiEiWUQAnIiIikmUUwImIiIhkGQVwIiIiIllGAZyIiIhIllEAJyLSCzOLmtkmM9tqZi+Z2UfNrM/PTTObamb/kq42isiZSQGciEjvWpxz851z5wKX4e3ycFs/r5kKKIATkZRSHTgRkV6YWaNzrrDL/bPwKtqPBabgFcgtiD98k3NunZmtB+YAu4H7gf8CvohXeHsU8C3n3HfSdhEiMiIpgBMR6cWpAVz8WB0wC2gAYs65kJmdDTzgnFtkZpcCH3POvS3+/DXAOOfcHWY2CvgbcJVzbndaL0ZERpRAphsgIpKlgsBdZjYfiAIze3neKuB8M7syfr8EOBsvQyciMiQK4EREBig+hBoFavDmwh0B5uHNJw719jLgQ865x9PSSBE5I2gRg4jIAJhZOfBt4C7nzT0pAQ4552LAtYA//tQGoKjLSx8H3m9mwfh5ZppZASIip0EZOBGR3uWZ2Sa84dII3qKFr8Ufuxt42MyuAx4DmuLHNwNRM3sJ+AHwTbyVqS+YmQG1wD+m6wJEZGTSIgYRERGRLKMhVBEREZEsowBOREREJMsogBMRERHJMgrgRERERLKMAjgRERGRLKMATkRERCTLKIATERERyTL/CxiR/HPM2fguAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmp1pSbaunlf"
      },
      "source": [
        "That looks much better!\n",
        "\n",
        "Do you see what's happened here?\n",
        "\n",
        "We're going to be using the training set (past) to train a model to try and predict values on the test set (future).\n",
        "\n",
        "Because the test set is an *artificial* future, we can guage how our model might perform on *actual* future data.\n",
        "\n",
        "> 🔑 **Note:** The amount of data you reserve for your test set not set in stone. You could have 80/20, 90/10, 95/5 splits or in some cases, you might not even have enough data to split into train and test sets (see the resource below). The point is to remember the test set is a pseudofuture and not the actual future, it is only meant to give you an indication of how the models you're building are performing. \n",
        "\n",
        "> 📖 **Resource:** Working with time series data can be tricky compared to other kinds of data. And there are a few pitfalls to watch out for, such as how much data to use for a test set. The article [*3 facts about time series forecasting that surprise experienced machine learning practitioners*](https://towardsdatascience.com/3-facts-about-time-series-forecasting-that-surprise-experienced-machine-learning-practitioners-69c18ee89387) talks about different things to watch out for when working with time series data, I'd recommend reading it.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGUelYVKCybB"
      },
      "source": [
        "### Create a plotting function\n",
        "\n",
        "Rather than retyping `matplotlib` commands to continuously plot data, let's make a plotting function we can reuse later."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trkvizA0C6vc"
      },
      "source": [
        "# Create a function to plot time series data\n",
        "def plot_time_series(timesteps, values, format='.', start=0, end=None, label=None):\n",
        "  \"\"\"\n",
        "  Plots a timesteps (a series of points in time) against values (a series of values across timesteps).\n",
        "  \n",
        "  Parameters\n",
        "  ---------\n",
        "  timesteps : array of timesteps\n",
        "  values : array of values across time\n",
        "  format : style of plot, default \".\"\n",
        "  start : where to start the plot (setting a value will index from start of timesteps & values)\n",
        "  end : where to end the plot (setting a value will index from end of timesteps & values)\n",
        "  label : label to show on plot of values\n",
        "  \"\"\"\n",
        "  # Plot the series\n",
        "  plt.plot(timesteps[start:end], values[start:end], format, label=label)\n",
        "  plt.xlabel(\"Time\")\n",
        "  plt.ylabel(\"BTC Price\")\n",
        "  if label:\n",
        "    plt.legend(fontsize=14) # make label bigger\n",
        "  plt.grid(True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "6TyFY1cGC_sw",
        "outputId": "41e56d50-31b2-4c88-9d78-7095f6e74dd8"
      },
      "source": [
        "# Try out our plotting function\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_time_series(timesteps=X_train, values=y_train, label=\"Train data\")\n",
        "plot_time_series(timesteps=X_test, values=y_test, label=\"Test data\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDeel1jj8ioU"
      },
      "source": [
        "Looking good!\n",
        "\n",
        "Time for some modelling experiments."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwSpQQRl_dHb"
      },
      "source": [
        "## Modelling Experiments\n",
        "\n",
        "We can build almost any kind of model for our problem as long as the data inputs and outputs are formatted correctly.\n",
        "\n",
        "However, just because we *can* build *almost any* kind of model, doesn't mean it'll perform well/should be used in a production setting.\n",
        "\n",
        "We'll see what this means as we build and evaluate models throughout.\n",
        "\n",
        "Before we discuss what modelling experiments we're going to run, there are two terms you should be familiar with, **horizon** and **window**. \n",
        "  * **horizon** = number of timesteps to predict into future\n",
        "  * **window** = number of timesteps from past used to predict **horizon**\n",
        "\n",
        "For example, if we wanted to predict the price of Bitcoin for tomorrow (1 day in the future) using the previous week's worth of Bitcoin prices (7 days in the past), the horizon would be 1 and the window would be 7.\n",
        "\n",
        "Now, how about those modelling experiments?\n",
        "\n",
        "| Model Number | Model Type | Horizon size | Window size | Extra data |\n",
        "| ----- | ----- | ----- | ----- | ----- |\n",
        "| 0 | Naïve model (baseline) | NA | NA | NA |\n",
        "| 1 | Dense model | 1 | 7 | NA |\n",
        "| 2 | Same as 1 | 1 | 30 | NA | \n",
        "| 3 | Same as 1 | 7 | 30 | NA |\n",
        "| 4 | Conv1D | 1 | 7 | NA |\n",
        "| 5 | LSTM | 1 | 7 | NA |\n",
        "| 6 | Same as 1 (but with multivariate data) | 1 | 7 | Block reward size |\n",
        "| 7 | [N-BEATs Algorithm](https://arxiv.org/pdf/1905.10437.pdf) | 1 | 7 | NA |\n",
        "| 8 | Ensemble (multiple models optimized on different loss functions) | 1 | 7 | NA | \n",
        "| 9 | Future prediction model (model to predict future values) | 1 | 7 | NA| \n",
        "| 10 | Same as 1 (but with turkey 🦃 data introduced) | 1 | 7 | NA |\n",
        "\n",
        "> 🔑 **Note:** To reiterate, as you can see, we can build many types of models for the data we're working with. But that doesn't mean that they'll perform well. Deep learning is a powerful technique but it doesn't always work. And as always, start with a simple model first and then add complexity as needed. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO1YAv7w_dE7"
      },
      "source": [
        "## Model 0: Naïve forecast (baseline)\n",
        "\n",
        "As usual, let's start with a baseline.\n",
        "\n",
        "One of the most common baseline models for time series forecasting, the naïve model (also called the [naïve forecast](https://otexts.com/fpp3/simple-methods.html#na%C3%AFve-method)), requires no training at all.\n",
        "\n",
        "That's because all the naïve model does is use the previous timestep value to predict the next timestep value.\n",
        "\n",
        "The formula looks like this:\n",
        "\n",
        "$$\\hat{y}_{t} = y_{t-1}$$ \n",
        "\n",
        "In English: \n",
        "> The prediction at timestep `t` (y-hat) is equal to the value at timestep `t-1` (the previous timestep).\n",
        "\n",
        "Sound simple?\n",
        "\n",
        "Maybe not.\n",
        "\n",
        "In an open system (like a stock market or crypto market), you'll often find beating the naïve forecast with *any* kind of model is quite hard.\n",
        "\n",
        "> 🔑 **Note:** For the sake of this notebook, an **open system** is a system where inputs and outputs can freely flow, such as a market (stock or crypto). Where as, a **closed system** the inputs and outputs are contained within the system (like a poker game with your buddies, you know the buy in and you know how much the winner can get). Time series forecasting in **open systems** is generally quite poor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCKGUO4OVos9",
        "outputId": "4b879155-2d44-4b95-fe4c-e71d2d96b968"
      },
      "source": [
        "# Create a naïve forecast\n",
        "naive_forecast = y_test[:-1] # Naïve forecast equals every value excluding the last value\n",
        "naive_forecast[:10], naive_forecast[-10:] # View frist 10 and last 10 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([9226.48582088, 8794.35864452, 8798.04205463, 9081.18687849,\n",
              "        8711.53433917, 8760.89271814, 8749.52059102, 8656.97092235,\n",
              "        8500.64355816, 8469.2608989 ]),\n",
              " array([57107.12067189, 58788.20967893, 58102.19142623, 55715.54665129,\n",
              "        56573.5554719 , 52147.82118698, 49764.1320816 , 50032.69313676,\n",
              "        47885.62525472, 45604.61575361]))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "a0NLHXDhWDX-",
        "outputId": "4adddc06-70f1-4f4b-b620-285a43b80c9a"
      },
      "source": [
        "# Plot naive forecast\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_time_series(timesteps=X_train, values=y_train, label=\"Train data\")\n",
        "plot_time_series(timesteps=X_test, values=y_test, label=\"Test data\")\n",
        "plot_time_series(timesteps=X_test[1:], values=naive_forecast, format=\"-\", label=\"Naive forecast\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VGukeuMSTkR2YapyfG0jeovQP1+/38+CDD3LZZZdVqWvdujVut5tPP/2UefPm8emnn/Lqq69y77338uWXX5KVlXVcYxkzZgxbt27l2WefJTMzk5SUFAYMGBD2Pl51xo8fz6xZs5g0aRJdunShYcOGjB49+rD3iYiIHInyHriDm7/lYMFGyMwIr/cF9kK1G+fzy69/y/kHinjmq2eiurSIErhIyMiO+loxvXr1YtWqVZx88smHvMYYw1lnncVZZ53FxIkTOe2003jnnXfIysoiOTkZn893yHvLdevWjXnz5nHttdcGy+bNmxd2zdy5c3n++ee56KKLANi6dWvYbFmApKSkKp83d+5crrzySoYNGwYEluJYu3YtXbt2PWxcIiIiteVbPRuAVW7LdSe0qVqf0ggAb/5XAMxOS4Xtu6L2mhQogYtbEydOZMiQIXTq1InLL78cj8fD999/T15eHk8++STz5s3js88+44ILLiA9PZ1vv/2WgoICunfvDgRmhm7YsIHFixfTsWNHGjduTEpKSpXPuf322xk9ejQ///nP6d+/P++//z7z58+nRYsWwWu6du3KG2+8wZlnnsmBAwe4++67SU5ODntOZmYmc+bM4dxzzyUlJYXmzZvTtWtXZs6cyeWXX05SUhIPP/wwxcXFkW04ERFJOPbHijVPv6/m7zqbnAaAt2NfWP96oNDljurODPXvrTw5Li644AI++ugjvvjiC7Kzs8nOzubxxx+nY8eOQGDR4q+//pohQ4bQpUsX7rzzTh544AFGjhwJwLBhw7jwwgsZMGAArVu3Ztq0adV+zhVXXMFDDz3EhAkTOOOMM1i2bBm//e1vw6557bXXKCwspHfv3lx55ZVce+21VXZqmDRpEl988QUZGRnB9dKeeeYZWrduTb9+/Rg8eDB9+/alX7/6s42JiIjEB1/7nx+yrqHfj78ksKC8N2y2anS3X1cPXJwYPnx4+CwaYODAgQwcOLDa67t168asWYeetJuSksL7779fq8++9957uffee8PKHnrooeBxVlZWlR0dypcsKXfxxRdz8cUXh5V16tSJGTNmhC3kO378+FrFJCIiUlv+pu3D93YK0dLnw1+4FQDvhq9DbvJGdQhVPXAiIiKS0Pwb5gaPkyp1hrT1+vD7SqEgjz178ysqory5vRI4ERERSVwFeby5c3Hw1F+puqG1+JPT+PKd4fx6T8gkvShvbq8ETkRERBJXfi7fpVRMrPOZ8HfbkhqlY42LLxq4w++L8moTSuBEREQk4ZTkf03ZV09B8b4ar3O16orPuCislNixcErkgqsFJXAiIiKSWAry6PPlbxj+w6vwzZ/4xcGiQ17qxoUt3V91zum3r0c0xMPRLFQRERFJLPm5AKxLTgK/j9IaVgQx67/En+ypmjA1PqG6y+uMeuBEREQksYTOHnW58dawpluS38fGpCSMrVRx8vmRia2WlMCJiIhIYgmdgHDhpCoTF0J9mtYQgJyGqWHlRR/fDQV5EQmvNpTAiYiISOLqMxZfNcOhd+/czT07d1HkCqRK3ko5Xkn5Qr5RogROgh566CF69OgR8c/5+uuv6dmzJ8nJyfTv3z/inyciIlITn6tqD1z/gwe5el8hGWVlALT3esPqjStJC/nK0Rs7dizGGB599NGw8pycHIwx7Nixo9bPGj9+PF9++eXxDrGK22+/naysLNauXcv06dMj/nmRZIyp9ZZjIiJST1Qa+vQnpdG/qITBhQeDZUmuJGjfm4d27gGgQaV34Py/elAL+cqxadCgAU899RTbt28/puc0atSIli1bHqeoDm3NmjWcd955ZGRk0KJFi6N6Rmlp6XGOSkREEkJBHnbqr8POvUkpuDL64m5bMQrlufwNGPQ4DU1g/mmZCU+Z/J8/qnfg5Nj88pe/JDMzs0ovXCifz8d1113HiSeeSGpqKl26dOHJJ5/E76/YNCR0CPXTTz8lOTmZnTt3hj3nvvvuo2fPnsHz//znP5x77rk0bNiQ9u3bc9NNN7FvX/WLIubn52OMYe/evVx77bUYY5gyZQoAX331FWeeeSYNGjQgPT2d//3f/w1L0vr3789NN93E+PHjad26NWeffTYAK1as4KKLLqJx48a0adOGESNGsGXLlrDPnTp1Kj/72c9ISUkhPT2dMWPGBOueeeYZevbsSVpaGu3bt+f6669nz549wfq9e/cyatQo2rRpQ4MGDejcuTOTJ08GIDMzE4DLLrsMY0zwXERE6rH8XHKSXWHnfr8fd1orXOkhCZzLDRnZuIc8C0Bps4ywx/j9ZVF9B07rwNXgibwnWLVrVZ1+5qktTuV32b87ontcLhePP/44l1xyCbfffjsnnXRSlWv8fj/t27fn3XffpXXr1uTl5TFu3DhatmzJddddV+X6AQMG0KpVK9577z1+85vfAGCt5a233uLmm28GYNmyZQwcOJCHH36YV155hV27dnHHHXdw7bXXVjusmJGRwebNmznppJN47LHHuOKKK2jatCmbNm1i8ODBjBo1iilTprB27Vquv/56XC4XDz30UPD+N954g3HjxpGbm4u1ls2bN/OLX/yC6667jqeffpqysjImTJjA0KFD+eabb3C5XLz44ovcfvvtPPbYY1x00UUUFhby+eefh7Xd5MmT6dy5Mxs2bOC2227jtttu4+9//zsA999/P8uWLWPmzJmkp6ezfv36YE/nggULaNOmDS+//DJDhgzB7a60zYqIiNQ/mf3Yv+D5sHPfjhzcxo3rYEWnhWva1TD6Q9wnZAFQ5gr/b7w/ypvZK4GLExdeeCFnn302EyZM4O23365Sn5SUxCOPPBI8z8zMZPHixUybNq3aBM7tdnPllVfy5ptvBhO4r7/+moKCAq666ioAnnrqKa644gruvPPO4H1/+ctfOOOMM9i2bRtt2rSp8sy2bdtijKFp06a0bdsWgBdeeIF27drxwgsv4HK56NatG48//jg33ngjd999N40bNwbgxBNPZNKkScHnTZw4kaysLJ544olg2euvv06LFi1YuHAh2dnZPProo9xxxx389re/DV7Tu3fv4PEdd9wR1iZPPvkkQ4cOZerUqbhcLjZs2ECvXr3Izg6859CpU6fg9a1btwagWbNmwe8iIiL1XEY2yb+4C5a/GDz3LfDhdrlxFW4LXtbAWwL5uXh6/g8AO4vCX1PyD381qu/AKYGrwZH2hEXbE088wVlnncVdd91Vbf1f//pXXnnlFTZs2EBRURFlZWVhCUllI0eOZPLkyWzYsIFOnTrx5ptvcu6559KhQwcAFi1axJo1a3jnnXeC91gbeMtz7dq1VRK4Q1m5ciV9+/bF5aro0j7nnHMoLS1l3bp1pKenA+GJV/nnf/XVVzRq1KjKM9euXUtmZiabNm1iwIABh/zszz//nD/84Q+sXLmSvXv34vP5KC0tZcuWLbRr146bbrqJ4cOHs2jRIs4//3wuvvhizj333Fp9LxERqZ+S2nSD5c5JQR4+vy/QA+cJrPXWq7iEZKeHzeO8A1fkKwl7hv+EnkST3oGLI9nZ2QwbNoy77767St0777zDHXfcwdixY/nkk09YsmQJN998c42TAXr16sWpp57KW2+9RVlZGe+99x4jR44M1vv9fq6//nqWLFkS/Fm6dCmrV6/m9NNPPy7fyYQsrpiWlhZW5/f7ueiii8I+f8mSJaxevZohQ4Yc9tkbNmzgoosuolu3brz33nssWrSI1157DaiYJDF48GA2bNjA+PHj2bFjBxdddBHXXHPNcfluIiISHUk711ecTP01Pm8R7oO78BXMD9RbYNDjgXfgXNW/HhP6Dnk0qAcuzjz22GN0796djz/+OKx87ty5nHnmmdx6663BsrVr1x72eSNHjuTNN9+kR48eHDhwgOHDhwfrevXqxfLlyzn55JOPKeZu3brx7rvv4vf7g71wc+fOJTk5mRNPPPGQ9/Xq1Yt3332XTp06kZSUVKW+cePGtG/fnjlz5nD++VW3PFm4cCGlpaU8++yzwffXZs6cWeW6Vq1aMWrUKEaNGsXgwYMZMWIEf/3rX0lJSSEpKQmfz3e0X11ERKIgZXvI++2+UnylB3Bt/o5/NAr0wK1PcsOWJQDBHrjK/EQ3gVMPXJw5+eSTGTduHM8991xYedeuXVm8eDGzZs1i9erVPProo7Va8+3qq69mxYoVPPDAA1x88cU0adIkWPe73/2OvLw8fvOb3/Dtt9+yZs0aZs6cyY033nhEMd9888389NNP3HzzzaxcuZKPPvqIe+65h1tvvZWGDRse8r5bbrmFvXv3csUVVzB//nzWrVvHZ599xrhx49i/fz8AEyZMYPLkyTz77LP897//ZcmSJcH36Lp06YLf72fy5MmsX7+eadOmBWeYlps4cSL//Oc/Wb16NStXrmT69Ol07tyZlJQUIPDe3Jw5c9iyZQu7d+8+ou8tIiLRYdqGDH+63Pi9xXj2/RQs8mPg27egIO+QPXA+G93/eVcCF4cmTpyIxxP+fww33ngjl19+OVdddRU///nPyc/PD5t8cCidOnXinHPOYenSpWHDpwA9e/bkq6++Ij8/n3PPPZesrCzuvffe4DtrtdW+fXtmzZrFt99+y+mnn861117LiBEjeOyxx2q8r127dnz99de4XC4GDRrEaaedxi233EJKSkowwbrpppv485//zMsvv0yPHj0YNGgQy5cvD8b/3HPP8cwzz9C9e3deeeUVnn766bDPSElJYcKECWRlZXH22Wezf/9+/vWvfwXrJ02axBdffEFGRgZnnHHGEX1vERGJDn/rrhUnXQbiBVy2YqXeVOsHZ5kQz+bvqn2GtZV3t69bJtoB1LU+ffrYhQsXVlu3cuVKunXrVscRVW///v3B2ZeJLF7a4Xj82crJydHWY6gdQqktAtQOFdQWAYdrh/8s/Rs3LnkGgGUbt3BmhzYM23+AVL+fl5o3pWNZGR/9tB0unEThJ/dyVodWVZ7xj1//g67Nu1YpP96MMYustX0ql6sHTkRERBKK3bKs4sTvw+fy4EnvQQ9voFPLYODCSVC0E7ev+sl+0e4AUwInIiIiCcWfflrFiTsZrwFPl4G4BzwIgG3SDvqMDSwj4qo6SQ70DpyIiIhInbKtulQcj/4Qn/XjcXnwpHcPFHoaBH5nZOO+4A/VP2PL95EOs0ZK4ERERCSh+G3FEiDeDr0AcBt3tUuGuIp3hZ0/tD2w3Zbvp8URjPDwlMBVEu0xbYk/0V7sUUREwoUlcH4vAB6Xp/olQyrtd9rWWftTOzHUIw0aNGDnzp1K4uS4sNZSWlrKpk2bquwiISIi0RP697zPH0jIPC4P7u0/BAq9xRUXh+x3OmbvPoxzq7/1qRGPsybaiSFEhw4d+PHHH9m+ffvhL46w4uJiGjRoEO0woi7W28Hj8dC0aVNatao6BV1ERKIjdAJCsAdu7yaScl+Ats1h309QkFdls/qfF5fhdicD4b140aAELkRSUlKNWzfVpZycHC0Mi9pBRESOv9BtsLzWSeB2r8fjLBniA8jPrZLAmawrcJ1wBnz7lBI4ERERkboUOoRa3gPnbtmVNGcSQ4nLVHn3DcDVYxguT2C/VO2FKiIiIlKH/DtWB4/Lh1M9rbrQ6Io3ACj2JFd7n8Hg2rYq8IytKyIcZc2UwImIiEjiKMjjj9/9NXjq3RRYDsRt3KS5A+9cn1hcBFN/HXgPLoRZPRvXrN8B4P/swSr1dUlDqCIiIpI48nPZ5KlYLsT3YyAJS3IlkbTxG17dsp0uJSVgTeA9uBCuvBdxlS8j4vdW+55cXVEPnIiIiCSO1JZhp2Ul+wFw71oHmf3ILoPm1oA7OfAeXEgSZ/wWlwmkTn5XUrXvydUVJXAiIiKSOIp20rU0MNs0xe/H+93bAHi+eDxQP2YGnDch8DsjOyxJc7mTcJ39vwD4zrsvar1voAROREREEklmP35WGhgGTbHgc2akuv1lFUOi/e6sSM5CkjRzwWO4TrsEANvy5LqNuxIlcCIiIpI4MrLxdzkfAJuUitedBBDYB/UwQ6Ku9NOCQ6ihiwFHgxI4ERERSSj+hoH34Kw7Cd+ZNwKQlD3usEOixhjcJjABItrbbiqBExERkYRSvouC9XuZtuJ1AB6DZZYAACAASURBVNzz/3LYZUHMqlkYY4Do98BpGRERERFJKOW7KJT4SpjdMLD2m8dXBkvfqtoLt3BK8ND19bO43c5ODFHeSks9cCIiIpJQ/P5A8uWnYhjUbS1gql688sPgobFg1s4J3BvPCZwxJt8Ys8wYs8QYs9Apa2GMmW2MWe38bu6UG2PM88aYNcaY74wxvUKeM8a5frUxZkxIeW/n+Wuce6tpeREREZEK5T1woSmYx5UMWSOqXtxtKKeUBJYdcQHukwcG7v3vrKjuxFAXPXC/tNaebq3t45zfA8yx1nYB5jjnAIOBLs7POOAvEEj4gAeBM4Fs4MHypM+55oaQ+wZF/uuIiIhILKuu98xzyQvVT2LoMxZX0w6B437jcbXuCsCuH/5d7XZbdSUaQ6hDganO8VTgkpDy123APKCZMeYE4AJgtrV2l7V2NzAbGOTUNbHWzrOBqSCvhzxLREREpFo+f9UJCJ52Zxz6BmfWKt2G4Fo5A4DJLZqCr7TKdlt1JdKTGCzwqTHGAi9aa18C0q21m536LUC6c9weKAi590enrKbyH6spr8IYM45Arx7p6enk5OQcw1eqG4WFhTERZ6SpHSqoLQLUDhXUFgFqhwpqi4DDtcP2HdurlC2Yv4D1Seurvf5A4QEAVnz5Pv9n5XuQ0RYAPy6W7EpjXxTaPNIJ3DnW2k3GmDbAbGPMqtBKa611kruIchLHlwD69Olj+/fvH+mPPGY5OTnEQpyRpnaooLYIUDtUUFsEqB0qqC0CDtcO7815L7wLCDjn/5xD27S21V7/8kcvs3HHRro2OUg7ZzP7M4uKcfUeSa8hvzleYR+RiA6hWms3Ob+3AR8QeIdtqzP8ifN7m3P5JiAj5PYOTllN5R2qKRcRERGpXkEe/p1rqxR7tiw75C3lcyTtCVkYdzIZZV5a+oGsqyIV5WFFLIEzxqQZYxqXHwMDge+BGUD5TNIxQPn83BnAaGc2al9grzPU+gkw0BjT3Jm8MBD4xKnbZ4zp68w+HR3yLBEREZFwBXkw9dfMLara3+N5e+QhJyQEt89qfQqMmYG7YQv8J/aL6mb2kRxCTQc+cLJWD/CWtfZjY8wC4F1jzHXABuBy5/p/AxcCa4CDwDUA1tpdxphHgQXOdY9Ya3c5xzcDU4BUYJbzIyIiIlJVfi7/dVW/fpvHW1KxmX0l5Qmc3/ohIxtPWht8qc0iGurhRCyBs9auA7KqKd8JDKim3AK3HOJZrwGvVVO+EOhxzMGKiIhI/MvsR9l/nq62ygOQ2rLaOuMs8Fu+/6nbuPH6vZGIsNa0E4OIiIgkhoxsPKdcVG1VAwwU7ay2zlVSCIB/2woA3C531PdCVQInIiIiCcN9iAQOVxJk9qtaXpDH6RsWAdBq1gQoyMNt3NWuJVeXtJm9iIiIJAx/29MOUXOIVc3yc7l59x4G7d/HyV4/5OcGhlCthlBFRERE6oTv+w+qr/D7qt9VIbMfbncyXbx+cCcHzssO4t+9Ie73QhURERGJvoI8CuY/D8Dl+/YHi88/cBBcnuqHUDOyYcwMOG9C4Dfg2fI9vj0boroXqoZQRUREJDHk53Jn6xYAdCstDRbfuHsfnHH1odd1y8iuqMudhNv6KTKmYi/UKKwHpx44ERERSQwhy4T4naVBADrgqf2uCpn9cGPwGRMcUo0GJXAiIiKSGEKWCbEhCZy5+t3a96JlZOPOOBNf47aBIdUo7cagBE5EREQSQ0hvmd/lDh67Ovz8iB7jbtgSb8MWUd1KSwmciIiIJJzQRUPKt8qqLbdxB7bViiIlcCIiIpIY8nPJKi4B4JL9FbNQzY8Lj+gx2olBREREpI74GjRnaYMUABqG7KTg+vulR7QciMd4tBeqiIiISF0oObgt5KxiEoOrfDmQWlIPnIiIiEgdsR3PqjhxJwUPzREuB1If9kJVAiciIiIJwdvu9IqTsR9VHB/hciBuox44ERERkToR9t7aMSwB4na59Q6ciIiISF0I6zULnbRwhHuaug/swF9WpM3sRURERCIt7L210EkLRzKJoSAPz/IP8flKtJm9iIiISKSFDXuG7IuKy137SQz5uVxQWEjXkoPazF5EREQk0rw/fVtxMuuukBpT5dpDyuxHD5/h4gMlUd3MXj1wIiIikhB8q/4FwF07d4OvjH4Hi8htmAp+b+170jKyA7NW83MDyVuU9kNVAiciIiLxryAP36qZcEIr2nu94PLw3I59lNndR96TlpEd1Y3sQQmciIiIJIL8XLzOBvQugF4jScq6iqQo96QdLSVwIiIiEv8y+2G/eRYAtysZsq6qFz1pR0uTGERERCT+ZWTjv/DpwPGAiTGbuJVTAiciIiIJwaZ3A8C0PiXKkRw7JXAiIiKSEKy1ALhM7Kc/sf8NRERERGrBEkjgzJGs+1ZPKYETERGRhFDeAxcH+ZsSOBEREUkM5T1wxzyEWpAHuZOiupm9lhERERGRhOB31oE7piHUgrzAJva+0sACwGNmaC9UERERkUg7ph64/NxA8mZ9FZvZR4ESOBEREUkI5T1wxySzX6Dnzbi1mb2IiIhIpB2XWajazF5ERESk7hy3deDqwRZcGkIVERGRhFCewBkT++uIKIETERGRhKCFfEVERERiTDCBUw+ciIiISGw4LuvA1RNK4ERERCShaDN7ERERkRihHjgRERGRGKPN7EVERERiTHAz+zhIf2L/G4iIiIhUVpAHuZMCvx3xtA6cdmIQERGRuNJk7yq2vPEIDbylNDNPBba+ysjWOnBHwhjjNsZ8a4yZ6ZyfaIyZb4xZY4x5xxiT7JSnOOdrnPrMkGfc65T/YIy5IKR8kFO2xhhzT6S/i4iIiNR/zfZ8z/ntWnJ+h3TwlgT2LQX8O/4LgNm6PJrhHRd1MYR6O7Ay5PwJ4Flr7cnAbuA6p/w6YLdT/qxzHcaY7sCVwGnAIOAFJyl0A38GBgPdgRHOtSIiIpLAypIaA1DscgF+SG0JBXncvewFAMyM28KGVmNRRBM4Y0wH4CLgFefcAOcB7zuXTAUucY6HOuc49QOc64cCb1trS6y164E1QLbzs8Zau85aWwq87VwrIiIiCSypbH/FiXFB0U7Iz8XnvPvm8pUFe+ViVaTfgZsM3A00ds5bAnustV7n/EegvXPcHigAsNZ6jTF7nevbA/NCnhl6T0Gl8jOrC8IYMw4YB5Cenk5OTs7Rf6M6UlhYGBNxRpraoYLaIkDtUEFtEaB2qKC2CPCknAQlXwLgx82SXWlh9X5cLN6Vxr4YbquIJXDGmCHANmvtImNM/0h9Tm1Ya18CXgLo06eP7d8/quHUSk5ODrEQZ6SpHSqoLQLUDhXUFgFqhwpqi4AF/1wBJYFjl8vQq1cvyMiGqX8GwH3pi/Q6NbYH7SI5hHo28GtjTD6B4c3zgOeAZsaY8sSxA7DJOd4EZAA49U2BnaHlle45VLmIiIgksEZ7l1Wc+H1VhkvNCT3rOKLjL2IJnLX2XmttB2ttJoFJCJ9ba68GvgCGO5eNAT50jmc45zj1n9vAgi0zgCudWaonAl2APGAB0MWZ1ZrsfMaMSH0fERERiQ27mnarOHEnQ2a/sPryLbViWTTWgfsd8LYx5v8B3wKvOuWvAn83xqwBdhFIyLDWLjfGvAusALzALdZaH4Ax5lbgE8ANvGatjf15wSIiInJM9jY5GfY6J84acKG8fm/Vm2JMnSRw1tocIMc5XkdgBmnla4qByw5x/++B31dT/m/g38cxVBEREYlxwT1PDyEeeuC0lZaIiIjElbR9PwSP7dRfV1nzTQmciIiISD3TaF/F/gFebzEsfSus3rdlWeVbYo4SOBEREYkru5t0DR4/0qo5fPsWLJwSLEv96E7txCAiIiJSn+xr3Dl4/M/GjcDvhZUf0rs4sDhcl+KimN+JQQmciIiIxJUq77i5k6HbUJItZBWXgMtTZWmRWBONZUREREREIsZSaRbqmMAysT4TWHeMyvUxSD1wIiIiElf8VOqBy8iG/FxKgGTrr3Z3hlijBE5ERETiSrXLhKS2ZF1yEo38Ni6GUJXAiYiISFyp0gNXkMfOT37HfpeLz9IagtaBExEREalfquzEsHQaB21g+6x0rzcwK1VDqCIiIiL1R5UeuMWv43MF5m3esWtPtRvcxxolcCIiIhJXqiRwfi++E34GgKfzL2HszCob3McaJXAiIiISVyoPoX7WMBXv5u8A8PS5NuaTN1ACJyIiInGmwYH1Yef/m94ar9Mr53HFxxK4SuBEREQkfhTkceLa16oUe40BwG3cdR1RRCiBExERkfiRn4v1+8KKkv0Wn5O4qQdOREREpL7J7IfXFd7L1qO0FG/HwHtvnh2roxHVcacETkREROJHRjZrOo8KK3Jh8G5aDIDn4/ugIC8akR1XtUrgjDGpxphTIh2MiIiIyLE60LB92HlZo9Z4bWBY1e0ri/lFfKEWCZwx5mJgCfCxc366MWZGpAMTERERORqV14HzpjbH50oCwONKivlFfKF2PXAPAdnAHgBr7RLgxAjGJCIiInLUKm9mX1ayF2+vkQB4fv18wqwDV2at3VupzFZ7pYiIiEiUVemBK9zGpu/eBMBj4uP1/9p8i+XGmKsAtzGmizHmj8B/IhyXiIiIyFGxlfqZygxMat4EAM8Hv0mYSQy3AacBJcBbwF7gjkgGJSIiInK0Ug5uCjsvcRbxBXD7SmHpW3Ud0nF32NXsrLUHgQnOj4iIiEj9VZBHu43vQavmwSJvyOK9Hgtgqt4XY2ozC3W2MaZZyHlzY8wnkQ1LRERE5Cjk52IrT2JwVaQ7blcyZI2o66iOu9oMobay1u4pP7HW7gbaRC4kERERkaOU2Q+fM1Hh4v0HOLOomLKQ6qQRbyXMLFS/MaZj+YkxphOahSoiIiL1UUY2W9oE1nn77a49nOCzwY3sIbE2s58AzDXG/N0Y8wbwFXBvZMMSEREROTp7mp4KgPsXd+LqMjC4CwOA560rEmMWqrX2Y6AX8A7wNtDbWqt34ERERKReKl8HznXWrZjiPWF1Sd6S+N5KyxhzqvO7F9AR+Mn56eiUiYiIiNQ75Qmc+6clfLZjaVidGxMXW2nVtIzIb4FxwKRq6ixwXkQiEhERETkWRYF14Fzf/4O97kp9VXGyE8MhEzhr7ThjjAu431r7dR3GJCIiInJ0CvL4Z/ECANxLpkHHtsGqFj4fWH9gCDXGZ6LWmIbawEIqf6qjWERERESOTcj7bS6/L6zqwR27wZ0cF0OotelHnGOMGWaMif1li0VERCS+hSRnbndyWJUHYNDjMd/7BrVL4G4E3gNKjDH7jDH7jTH7IhyXiIiIyJELSc5cgx4Pq3JbP6yZXdcRRURt9kJtXBeBiIiIiByz0DXePr4HMloHT93Wwv7NUQjq+KtpGZEuxpgPjTHfG2PeMsa0r8vARERERI5Y6Bpv3pKwKjfAGaPrNJxIqWkI9TVgJjAM+Bb4Y51EJCIiInK0QicouFw08VVsbO/peBb0GVv3MUVATQlcY2vty9baH6y1TwGZdRSTiIiIyNEJnaBw1q3YkCmY7p4j6j6eCKnpHbgGxpgzgPKvnhp6bq1dHOngRERERI5I6Dtw81+ETh3AHxhKNemnRSmo46+mBG4z8EzI+ZaQc+3EICIiIvVP6DtwvlICS9rGn5p2YvhlXQYiIiIicqxsp3Ng3RRu2rMP3MlYlxt8ZYE6bJSjO37iY0MwEREREcDXoTcArhPPhTEzsCF7nyqBExEREamH/M6QqTuzH2RkY23Fdlp26/JohXXcKYETERGRuOFzEjaXcUFBHqce2F9R+dFd4ZMcYlhNC/leYIwZXk35cGPM+Yd7sDGmgTEmzxiz1Biz3BjzsFN+ojFmvjFmjTHmHWNMslOe4pyvceozQ551r1P+gzHmgpDyQU7ZGmPMPUf21UVERCTeBHvgjBvyc/nj1m3ct2MX/Q4W0b3oQPgkhxhWUw/cRODLaspzgEdq8ewS4DxrbRZwOjDIGNMXeAJ41lp7MrAbuM65/jpgt1P+rHMdxpjuwJXAacAg4AVjjNsY4wb+DAwGugMjnGtFREQkQYX1wGX2o6nxMGJ/IS9s3U6yOzl8od8YVlMCl2Kt3V650Fq7A0g73INtQKFzmuT8lC8/8r5TPhW4xDke6pzj1A8wxhin/G1rbYm1dj2wBsh2ftZYa9dZa0uBt51rRUREJEH5/U4PnMsdWNR37EfQ5xrocy2MnRm+0G8Mq2kduCbGGI+11htaaIxJAlJr83Cnl2wRcDKB3rK1wJ6QZ/4IlO+x2h4oALDWeo0xe4GWTvm8kMeG3lNQqfzMQ8QxDhgHkJ6eTk5OTm3Cj6rCwsKYiDPS1A4V1BYBaocKaosAtUMFtQXs9wXeeVu7Zi05W3MChY2cvqK1B2FtTlTiOt5qSuCmAy8bY2611h4AMMY0Ap5z6g7LBqZ+nG6MaQZ8AJx6jPEeFWvtS8BLAH369LH9+/ePRhhHJCcnh1iIM9LUDhXUFgFqhwpqiwC1QwW1BWw/uB3eg1O7nkr/U/pHO5yIqWkI9X5gK7DBGLPIGLMIWA9sd+pqzVq7B/gCOAtoZowpTxw7AJuc401ABoBT3xTYGVpe6Z5DlYuIiEiC8m1aBIB7z8YoRxJZNSVw/7bW3kMgSRrr/HS01t5jrS073IONMa2dnjeMManA+cBKAolc+ezWMcCHzvEM5xyn/nNrrXXKr3RmqZ4IdAHygAVAF2dWazKBiQ4zavWtRUREJP4U5OF9bywA7q+fj5slQ6pT0xBqawBrbRGw7CiefQIw1XkPzgW8a62daYxZAbxtjPl/wLfAq871rwJ/N8asAXYRSMiw1i43xrwLrAC8wC3O0CzGmFuBTwA38Jq1Nn5W6BMREZEjs3QaO0xgFmqrshJYOi1uJi1UVlMC19QYc+mhKq21Nb4HZ639DjijmvJ1BGaQVi4vBi47xLN+D/y+mvJ/A/+uKQ4RERFJFJbNnkBqc4LXC3G0dVZlNSZwwBDAVFNnqeVEBhEREZE60fZ0tqwOrFTW1uuDtqdHOaDIqSmB22CtvbbOIhERERE5FkU7OWjcADS0Bop2RjmgyKlpEkN1PW8iIiIi9VNmP3xuN25rMZ6UuNl1oTo1JXCjKhcYY1o5uyOIiIiI1C8Z2Xh7DAvsgzpmRtxOYICaE7hGxpgcY8x0Y8wZxpjvge+BrcaYQXUUn4iIiEitlaW1wmWS4zp5g5rfgfsTcB+ByQyfA4OttfOMMacC04CP6yA+ERERkVrzWV9gI/s4V9M39FhrP7XWvgdssdbOA7DWrqqb0ERERESOjNfvxY072mFEXE0JnD/kuKhSXfwurCIiIiIxy+v3Bt6Bi3M1DaFmGWP2EZiNmuoc45w3iHhkIiIiIkfI6/fiqrF/Kj4cMoGz1sZ/+ioiIiJxxXtgGym+osA+qHE8kSH+U1QRERFJDAV5lK7/koale2HKkLjezF4JnIiIiMSHpdPY7jK08fnA52xmH6eUwImIiEh8KNzGFo87sA8qEM9zLpXAiYiISOwryGPL2k/Z5vHQujyBi+PN7JXAiYiISOzLz2WLK7DbZ0evN1CWoJvZi4iIiMSGzH6UuQOLa6R7veBO3M3sRURERGJDRjbeCx4DYG+LbBg7U8uIiIiIiNR33pYnAfBTp2FxnbyBEjgRERGJEz4bmLyQ6HuhioiIiMQMrz8wecFl4j+9if9vKCIiIgmhPIFTD5yIiIhIjCjzlwHgNkrgRERERGKCd8dqAJruz49uIHVACZyIiIjEvoI8vP95HoCfrZwU1xvZgxI4ERERiQf5uZQ6s1Ab+LyQnxvlgCJLCZyIiIjEvtSWFLsCaU2yccX1LgygBE5ERERiXUEeBz+5h+XJga20CjpfF/cL+XqiHYCIiIjIMcnP5XfNG5GTlgpAk8L1UQ4o8tQDJyIiIrEttSVLGiQHT0/YMluTGERERETqtaKdNPL7g6fG+mDptCgGFHlK4ERERCS2pbbEYyuVFW6NSih1RQmciIiIxLYtS2nrC2yj1au4OFDWKD2KAUWeEjgRERGJbYXbKDIuTi8uYermbYCBrBHRjiqilMCJiIhIbGvUmh1uNx28gV64HS2z434ZESVwIiIiEtuyrsJrDB5rwZ1MQcdLox1RxGkdOBEREYltGdl4GzbH07wbDP0d+9YejHZEEaceOBEREYl5XmPwtOsV90On5ZTAiYiISMzz+r14XIkzsKgETkRERGKeEjgRERGRGJNoCVzifFMRERGJS9ZafNaHp2ABtI7vPVDLqQdOREREYpp34zcAeNbnwtRf02TvqihHFHlK4ERERCSmleV/BYDH+sBXSrM930c5oshTAiciIiIxzZvRFwAPLnAns6dZjyhHFHl6B05ERERimrddTwA8XQZC1jgt5CsiIiJS33n9gT1Qk7oM1EK+x8oYk2GM+cIYs8IYs9wYc7tT3sIYM9sYs9r53dwpN8aY540xa4wx3xljeoU8a4xz/WpjzJiQ8t7GmGXOPc8bY0ykvo+IiIjUTz6/DyChlhGJZA+cF7jTWtsd6AvcYozpDtwDzLHWdgHmOOcAg4Euzs844C8QSPiAB4EzgWzgwfKkz7nmhpD7BkXw+4iIiEg9VN4DpwTuOLDWbrbWLnaO9wMrgfbAUGCqc9lU4BLneCjwug2YBzQzxpwAXADMttbustbuBmYDg5y6JtbaedZaC7we8iwRERFJEGW2DACPSZwErk6+qTEmEzgDmA+kW2s3O1VbgHTnuD1QEHLbj05ZTeU/VlNe3eePI9CrR3p6Ojk5OUf9XepKYWFhTMQZaWqHCmqLALVDBbVFgNqhQqK2xU+lPwHww8ofaLixYUK0Q8QTOGNMI+AfwB3W2n2hr6lZa60xxkY6BmvtS8BLAH369LH9+/eP9Eces5ycHGIhzkhTO1RQWwSoHSqoLQLUDhUSrS3sgr9RuvKfrG3ZCYCsExrSv1f/hGiHiM5CNcYkEUje3rTWTneKtzrDnzi/tznlm4CMkNs7OGU1lXeoplxEJC4t2rCbP3+xhkUbdkc7FJHoWziFC757kj52Hdu/fwcAz5xHoUBbaR0TZ0boq8BKa+0zIVUzgPKZpGOAD0PKRzuzUfsCe52h1k+AgcaY5s7khYHAJ07dPmNMX+ezRoc8S0QkrizasJsRL33D05/8wIiXvlESJzL/L2z2BAYScxumAtChtAiWTotmVHUmkkOoZwOjgGXGmCVO2X3A48C7xpjrgA3A5U7dv4ELgTXAQeAaAGvtLmPMo8AC57pHrLW7nOObgSlAKjDL+RERiTv/WPwjpb7AGyelPss/Fv/IwOaHuUkkTvk3zmO82QE0BOCdJo1J9ls6lnmhcBs0im58dSFiCZy1di5wqHXZBlRzvQVuOcSzXgNeq6Z8IRD/+2WISMKr/B9TLXopiWzvnInMTmsYVnZSWVlCbS+lnRhERGLAae2a1ngukjBmP0jOzqqb1bf0BRbzpVHrOg4oOpTAiYjEgN0HS4O9bi7nXCQhffcuE1u3DJ52LQn8u7DD7QZckHVVlAKrW0rgRERiQN/OLUlJcuE2kJzkom/nloe/SSQetcikWXlvG3B6SQkA65M80PY07YUqIiL1R+9OzXnz+r78duApvHl9X3p30gwGSVC/epgGtmIJ2a6lgV0YXABbVyTMMiKJ9L6fiEhM692puRI3ka0rOGAq+p9a+HxM3LGTn5WUAhbyc4HeUQuvrqgHTkRERGJDQR67Z41nv7sifUn3+bissIhTy3zgToHMflEMsO6oB05EJEYs2rCbeet20rdzS/XESWJaOo3/JoX3PTX2W+g9BppmBJK3jGxYmxOd+OqQEjgRkRiwaMNuRrw8jzKvnySPi2k39I12SCJRYNntdoeVpBpPYOZpgkxeKKchVBGRGDB98Y+Uev1YoNTrZ/riH6Mdkkjdy7qKIndSWFGq3xulYKJLCZyISAywlc7/u3V/VOIQiaqMbIpO+5+wolRvGSx9K0oBRY8SOBGRGNCjXdOw7bMW5O/mxSXFjHp1Pm/N3xi1uETqVEEexatmAPC3zVu5fs9eAv1xibe5nN6BExGp596av5GJH35fpRfumy0+2LKD3NU7ALjqzI51H5xIXcrPpRg/AL2KS+hTXBKYeZo1IsqB1T31wImI1GOLNuxm4off4/VXTt/Czfp+cx1FJBJFqS05aAwN/H5criTocy2MnZlwExhACZyISL02b93OKslbm8bJXHJ6u7CywT1OqMuwROpeQR57P76bqU0bU+xy0pesEQmZvIGGUEVE6rW+nVtiCJ/E4DKGLumNuTDTw/6kZgzucYKGTyX+5efygyfkXTd/WWDXhQRN4NQDJyJSj/Xu1JyhlXrbtheWMunTH/hso5c7ftVVyZskhsx+7HGFrwFH8b7oxFIPKIETEannRp2VidvpeDCAtRa/hTI/TP7svyzasDuq8YnUia0r2OP8izBqr5O4bfkuigFFlxI4EZF6bt66ncEhVENgCNVFYFj16zU7uPqVeUriJL4V5LFr1p082qoFAHfs2hMo7zY0ikFFlxI4EZF6rm/nliR7XLgNJCe5eGRoD37WoSkAfgulZX7mrdsZ5ShFImjpNB5oVbH/bzJAi87QZ2y0Ioo6TWIQEannendqzpvX9w1uZA/w/aa9wXo/0LxhcpSiE6kLll3uSn1Oqc2rvzRBKIETEYkBvTs1p3enwF9Y415fiK/SsnC7D5ZGISqROpJ1FWU/zQKgW4nzZ/2M0VEMKPo0hCoiEkPemr+RT1dsDStzGYI9cyLxqsRZ+62J3w9n35HQw6egBE5EJGYs2rCbB/65rEr5gG7pwd45kbiUn8vJpWUAXLO3EBo0iXJA0acETkQkRsxbt7PK0KkB2jRO0SxUiW+Z/WiIoZ3Xy9llFjL7RTuiqFMCJyISI/p2bonLVC1/c/5GRryspUQkjmVkU3pCFkme1a9okQAAIABJREFUVBj0eMLuvhBKCZyISIz4Yct+/n97dx4fZXUvfvxzZiaZJBDCQDABQhJQRASKEmTRarEKCrVV1NsqVqyt27329SrV1nJtq9arlvZXa3tvrbhbWyLagksVUGzZFBMgLLKJQCALkEAWyJ7Zzu+PZyaZyUz2mcxM+L5fr7wyc55nnjnPyRC+Ocv3tN3T3vvU7nSzcntpn9dJiD6x7TWay3YSb6+H1Q9ByZZI1yjiJIATQogoV1BUzbPrDvHm1uIOz9MdHhUiRm17jROrH2RdUiKDXW5wNcOuNyJdq4iTNCJCCBHFCoqque2lPOxOd9DhUy+TgpumZPRdxYToCyVbcH7wAHOyRwJwwuLdC1X+XJEeOCGEiGJ5hZXYnW7cGrSGkYMTgp7n1sYQqxD9ytFNbLe29jV9raHJeJB+UYQqFD0kgBNCiCjm3UbL+8u6rKap3XNX7znRN5USoq8kDvULVB6s8izUaZSt4ySAE0KIKJaTZeOR6yZgMincGlzu9s+dMFxyY4l+pGQL7tU/5ZUU43Od4nIRB6BMkkYECeCEECLqVTfYcWvd6ayfmmZnn9RHiD5xdBN7zbApKRGApWWnjPJvPCNpRJAATgghop53GNWsIN6smJyREvS8DtY4iD7gXS0s+fhCJPty6nxW7lxot0Nc0lm/hZaXrEIVQogol5NlY9ldM8grrGzZ8/S2l/JodrhbeuXMJsWEEcEDOxE+BUXV5BVWYkuK5/H392J3uom3mFh21wzZ3qw9JVvg6CZjGLSjnrRR03AMvwg4wbLjZUaP0wXX9VElo58EcEIIEeW8QcKMMUNbgoJld83gjY+3kjp8FC99cgS31jz+/l7GpSdL4NBHvClemh1ulDJWCWuMpMp5hZXycwimZAv/eutmpjbUk6Li4I73Ogzi7LMegvU/xhqfDOdfBTe92IeVjW4SwAkhRBTzzQPn27OTk2Wj9tx49uo43Frj1uCQwKFP5RVWtvSCap8Jim4NtqT4iNUrmlUc+pBFqSnMaIznxbJKoyeugwCu2dUMQNxdH0PKmL6qZkyQOXBCCBHFfPPAeQM0X77z4+IsppYhVhF+7e1NC7Dn+BkAFi3fwUWPf8Si5Tv6sGZRqmQLtV+uAuCL+HjAjbu0oMNtseynDgBgLf+iL2oYUySAE0KIKNZZgOZNMzJxZApXjB0WoVqenbw9ocEojODtnZ3HOd3g4J2dx8/uIG7to1S8eg0vOcoAOG0281FSIpPtuyn56zeDB3HbXsP+6TMAxP/9+7L/aRsSwAkhRBTzBmiXnpfKI9dNCAgYCoqqeey9PewqPcNH+8q5eelmcvOLA86R1ZGhV1BUzc6S00GPTRiRwvovT/mVfby/vC+qFXX01leZdHwlV2Zl8F7ywJbyB9OMPzgKzcCnf/R/UckWdqx9iC3WOCxaY3M0yv6nbcgcOCGEiGIFRdU89s+9OJxu8o9UBSxSyCusxO5qnYClNfz87d2MS08GYMX2Uv6+rQSnSxNnMfHG3bI6MlTyCitxuoNn56tusDPr/GG8s/N4S1l9s4uCouqzrv1rtzwHAzs5qWy3//OPH2PhiDQARjqcRrBSdzIc1YtZ0gMnhBBRbOX2UuxOd8vqxpXbS/2OB5ssr4GlGw5z20t55OYX43Dpdl8ves47vB1MbaOD22dm+5Vp4PkNh8NfsWiy7TUO1Rz1K5rU1Oz3vMZs8l8FUrKFqpLPWp46JMFhUNIDJ4QQUexguf8G9W37e7yT5ds6WdNEsyNw363tMowaMjlZNr43M5ulGwsDjr2z8xiFFfUB5eUd7GXbL+U/xx2enjSAnMYmHq+oYluClUeHGfM5z5jMYLbgfHo85iFZqGHj2Z5gbXnNr095Fu4MlDmevqQHTggholRufjFbjrYGXGYT3DQlw++c9jonUhLjgm69ddYFEGFUUFTNC5sCgzeAsppmPtoXOOdtdOqAcFcrujgaWh4mu9y8VnaSTKeTG+vq2XmkGKU1Z0wmntZVXJxq4c2q3bi3vUJJnNG/9GlRCdOamkGZYfKCSN1FVJIeOCGEiFKr95zwez5xRErA/Kn2dl/YdLAiaPmZJudZOQ8rHFZuL6WdKXDtemfncarq7RRXNXDthHQWzxsfnspFi/SvQPPnAGwu9gzfX7YIbKMx5/2ZZHcdZ0wm3kgx5mw+mTqEJ1OHAGB1uxnk1mBNge/+Q/Y/bUN64IQQIkpNGD7I7/nMIDneqhvsQV/bblyhdUAuOdEz3YzdWmw8WMHRygaWbizkosc/Clg1HBYlW+D9RfD+j/s2HcdlPyLBrfne6RpAGcHb7F8Z+5lmX8ogt5sas4kRDmfAS5tNnhDl/GskeAsibAGcUuoVpdRJpdQen7IhSqm1SqmDnu82T7lSSv2vUuqQUupzpdQUn9fc4Tn/oFLqDp/yHKXUbs9r/lcpJdMchRD9SnJiXIfPwZhI33Ye/RVjU4NeTyHJfkNpYgj2nj3d4ODht3ez8OX8ENSoHSVb4NV5rNr/Jkd3vg6vXAPbXuv9NTc93XkwOGoadpOJuOyvwg8+MoI3r7pTJLs1HwwcwPG41gHBZJcxd3NxZZVR0BC8N/lsF84euNeAa9uULQb+pbUeC/zL8xxgLjDW83UP8BwYAR/wKDAdmAY86g36POfc7fO6tu8lhBAxre0K02ArTnOybLx576XMvjCNyRkpPDV/EtPbCdDSBlllk/UQaq/3syc2Hqxgyar9Ibuen11vcMAMPzsnlW+OGkEtGj54oOc9cSVbcPzlW/xwz3Pszb2xw+s43U7caOLb2bh+vzXwM/14RSUFR4q5rabOKBh/fc/q2c+FLYDTWm8EqtoUXw/8xfP4L8ANPuWva0MeMFgpNRy4Blirta7SWlcDa4FrPccGaa3ztNYaeN3nWkII0S9UN9hbFimYaD9gyMmy8eLCqXznkkxW7zkRsHLV6ysZgzsN3nLzi7n95fy+GdaLcbakeMw+Yz8Wc+8GgpblF/WyRu2oO8nNGcNbnv44bRhol7EPaVdtew3+Ot/4fnQTR01uNiQl8vMhA2DN4naDOHuxkQ4krras07dYU3KMm2tqubyxkXhlhoFpcN0fjeFWEaCvFzGkaa29s3LLAO/a4pFAic95pZ6yjspLg5QHpZS6B6Nnj7S0NNavX9/zO+gjdXV1MVHPcJN2aCVtYTib2sF62oVJgUuDUmA9XcT69a2/+nzbYn2xg9f2td8jpIBpyWc6bDvfa2w6WMGXBw4wKzNw2DbaROIzcajaxW+3NuHSRnB90Tlm5o2O460Ddr48HZi+pSvsTlev7yNYW1xytACGt/4c8xMTKLBaSf1yD0dcnb/f8OMfcvDYa7yXPIBn3/83ypSANhv3eDg+nt+fPsyPXp5D2fBrKE+/kpqUCwAYdOYLsj//BWQOJ37LK2yvTG85BjD2jJP/rqvi16lD+HPZSUY4XTxSWY1WcWy/6Anj3DqgB21yNvyeiNgqVK21Vkr1dA5od9/rBeAFgKlTp+pZs2b1xdv2yvr164mFeoabtEMraQvD2dQOeav249JGmgqXhqQRY5k1PbPluG9bvPxyPtD+XCEN/POYle+MyGSBzzV8PfOnT4DWIPCwYxCPzZre29sIu0h8JvauO4RTGxuto+Cqi8/j4jFD+fXWzUHPT0+2UlbbHPSYl1MrkkdPbuklXbJqP2v2lnVrtWpAW6x9lGp7OeCffub3QwazrORtsub8Z+cLBF58nJvPMeZVThmdyYXNzdx5prWX99XBg7iltpZ3Gz/jjt3rGHDtb6GxEqo+ZmucMdCX5rQzpXETXH9f63XPTWLBqx+zoNbb26tg6p2oybcypZeLFs6G3xN9vQq13DP8iee7d1+MY8Aon/MyPGUdlWcEKRdCiH4hWI6xtmlFfM2dOLzdY167Ss/w8Nu7g861ys0vZlepf1Lg2kZHF2t79rElxbekEHFr4/nK7aW4gnS+KeDZ7+YwOMgiFL8ynxXCS1btZ+nGwpbVqjOf+rj7e9mWbOGzgue4Isv47/KXFa2zmlJdLkB3bRg12f+ztc9q5afn+C+UuWbUSJ6zpfCnQYk0ffBj1uT9Dl20mcWeZL3n2x2B22WNmgZ3roapd8LU7xuLHK57RlacdlFfB3DvAd6VpHcA7/qUL/SsRp0BnPEMtX4IzFFK2TyLF+YAH3qO1SilZnhWny70uZYQQsS8vMLKgBxjHQVpC6Zn8tT8SUzOSGFatq3dLZ4Ant9U6BcMFBRV84t3dgect7P0TPgm1se49QdOBjxvb0hJY/w8Uwf6T9hXwEPXXkBCnAmz8l8hvGZv65yx+XGrWWx/hBUvPtG9IO7jx3jwnNbdC75VV8/moyWc32yn0eSZr3fo484XM5w3u8tvecxi4e70Yfz0nFR+fE4qJy0WzFqT7QxMEwIYwdp1f5DArQfCmUbkDeAzYJxSqlQp9QNgCTBbKXUQuNrzHGAVUAgcAl4E/gtAa10F/A+w1fP1uKcMzzkveV5zGFgdrnsRQoi+NmPMUBLiTCjApOC+K8a0O/TpNS49mQPltWw9Wo3L5WZati3oxHqtjb1SvTpKSOsbSIhWbXe0KK9p4qYpGcQHaW+zSTFjzFC+/9UxfuX3en6my+6awQNzxvmtEM4ckgTALaZ/UZbxEb/LPM0T5pc488mLXavgttegZAtjHK29qAlak4yJdJeLMyYTKwcO4MCJrfDqvI6DuN1vBi0ea7fzwgn/3SbWDUhip2cbrH8NMO7hp1WeoDN9YtfqLrokbHPgtNa3tnPoqiDnauD+dq7zCvBKkPJtgHwahBD9Uk6WjUeum8DqPSeYO3F4p8EbGL08zQ5j43uXhm1F1dxz+RgOV9Szts22Tmv3lbNk1X4WzxvfYULaYMN+Z6vc/GLe3FpM2qCEgC3MRqcOICfLxhv3zCSvsJKD5bXkFVaSOSSJn80dT06WrSU4a/sz9T3m5f2ZzDVv4YeegOj9gUnMOf4a8FDHFV1xN/kH38VuNVNmMZNtd3B7jWfOWuYMBjR8wcakRB4dZuX8ZjsrjpfBrtzgPWAr7qagrAB89jP1+kZdPTObmtlQVMpgt5s/2Abz6uDW5NPxbk1OcxPX1Hm207psUcf1Ft0iW2kJIUQUKiiq5vH392J3utl6tIpx6cmdpgCxJcX7BWNuDS9uKuSWacGDv+c3FjJ7QjoNze0MbwGfHzsjW29hBG8Pv+0dZj4TcLyy3lj8ESwY87VgeuAikoKiavIKK5kxZigHympZvedES4BYoQe1vF9uSjLfOn4MVtwNNwXviRt+/EMOH1nJXRkjWsp+WH2ab9fWGfuJDhrJwLp9Lce+bMnDFiQFSskW9O63+N5o//ourqxiydAhJHq6bYecPw8O/Zv7Tp9hjMPBeLuddKeLFLfPhMAROTJEGmISwAkhRBTKK6zE7nTj1uBwuskrrOw0iAqWJ86l4ct28sJpYMX2UnaWnG73mm5tnHO2B3CvfBJ803qvriwiCaagqJpbX/gMh0u3pIzxlapqeKb8FD9OG8Zeq5Uqk4khu9+CaXcHDYiGndrMu0mJfmUjvdtU5SyEwg3MaGri74OMvUeHuFzGsclBBs2ObqLcbAZgjN1BYbzRG/udmjqsWjOvrgGyLoNblkHJFpJenccNdfX+11AmGD0LFr7dvYYRnZIATgghotCMMUMxmxRul8bkmUPVlddYTOBssxLS7nRjUgSd51ZR20zmkCSOVja0e93c/GJumpLRpSTAv197gPpmF9dMSOMPt1zcaZ1jQW5+McdON7Z7/JJsW5eGuINZsb0Uuydqaxu8Aax2TePXDbt5qLKa3w618cthQ3m2/JSxejRIAHdq2KXo8sN+ZSOcLkDB5AVgTWHOp38g2eWm1mxioNsNA9KC945lX47dbIQJd50+Q3FcHJOam7EAN9fWgykOrn7MOHfUNLhzlVGvxKFGGpF2dl8QoSGb2QshRBRau7cMh+d/dIdLc6AseC9aW22DNLNJ8Z1LMrGYgu8SUFLVwMaD/vnjgq1g/c3qjlejeocYK+rsNDpcvLPzOIuW7+hSnaOZ974aHe0n551/cUa7xzpT0UluuOXuqzgdP5xbPHPYWga7E4MH9AmNZZRZ/OctZjocYB1kBFOzfwVjvs4nxaXcVFtHo1LQWBV8EcOoaTTNXwqANTGV+0/XcEVjM5gsRtqPO1f5B2ijpsHlDxo7J1z+oARvYSYBnBBCRJmComqWbvQfsntza+dbW60Ispr0snOHsmB6JrPGnRP0NfuDBIaDEgIHZ/aX1XT43sFy1K3/8lSHr4kFXWn3vccD58SF0rK4m4gDrqpvYJ813ki1fOijwBPXPsoXlat5Y9BAAD4/UsyGolJS3W4j15rXwrcxXXAdA9xuTlksuN0OYxFDEPZh5wNgvf5PRp62q35p5G6TtB8RJwGcEEJEmV8GycmWNiih09cF62Mrrmp/aLS9azwwe1xAeW2Ti/Me/oBZ/29d0FxkE4YPCiibdf6wgLJYUlBUze7SzoOzU530onUkNdna6Tmfp82HwZlc2dDIabOZMosFju8I7DXb/RY/STPafJTDgQKGYDJWf87+lf+5Q8/DroxPzPODB8H2vwbthWvM+zMA1l3LW3vYJHCLChLACSFElAkWdN37tXM7fd2NUzJoO1J67YR0oGuBArTO53pq/qSAY043HK1s4ObnNgcEcduLA4O69C4EndEsr7CSruxq2tW2DeamKRkESR3n596vnQsTbmxZcPCTc1J5Iq4RXp4Nr8+HTU8bwVdS67DqM+WeYfGZPwwM3gDKPudWz7BsozKB2xm4K8OKu/l34QcAJH/+D1j7aM9uUoSFBHBCCBElCoqq+Y+lm2m0u/zKL8pI6dIq0JwsG0/cMImRgxMYMiCe+64Y07KHZlcCBbNJ8bO5xvkLpmcyPj056Hne1ateufnFbDkaGMAt3VhIbn5xyznX/+kT7nl9W/e3hIoQYyFJ5+fdNKXnc+Bysmy8dd+lXJJtC9qDavZG5AmDGOLZp2u/NZ43ByVzXcZwdh37hOINT8HLc9An95PqdPGNunrGORyQPil48AYw/nrGOJyYtCZOa2NBQvblRiC4fAE8M4lT+/7B31KMntUL7XbYuazH9ylCT1ahCiFEFCgoqubm5zYHTao729OL1pVreHPHxVtMfq/LybLxPzdM4pF39+D0TJRTGMGYwuh58yac9appan8vVN9g48/rDrZ73rPrDlJcWe8zp+8M6w6cZPk9M2MjNUlHWY6BORem9fo+crJs/P2+S1vywW04cLIlIHa5NSu3l5Iz9XJsG37t97qiuDi+O8L4GWc4HJTGGYsXWtKGTL2r/Ted+j0AzHuexpV6HtxkbIzkeOVapmSP5H51morBKQDcdqbW+HlbEoNfS0SE9MAJIUQUeH7D4aCxgqWLKUQgeO44XwumZ/LmvTNZMD2zJXjD8z1Yr1hifPC/8ePMiht9ep0a2vQY+qppcvJ8mwUZDpcOqFs0yiusDEjtMXJwAmaTQmGs1u3K0HZX5WTZuP/K8zgvzb/nUwOMmkaqdUi7r/UGbwBfa/SkPGnspI2nfg9LXCKuC75hzGvblct7A4xh72dtg3nTkytufl2dcf7lD3brfkR4SQAnhBBRoPBUXdDyx6+f2OUenhljhhJvCdwY3VdOlo1D5bUBwaJL+w+LAnz/stEBr483mxiaFM/9ywpYsmo/ufnFJMab263TKFti0MDUlhRPQVE1z647REFRNbn5xdz+cn7LkGs0sCXFB5Tdf+VY3rp3Jj+5Zhxv3D0jLL2I3j1VFRBvVi1DtPFxrXPtXjtezv+VBV/lO6nZDihjSLQTZmXG6XYa89s+/wdNQdLNpDpdkJLZ0msnooMMoQohRBQYPWwgh075Z7FXGBvUd1VOlo1ld81o2ZapveDi0MngweKhNjs2LJieSXFlPc9vLGwJwuwuN2WeVZdtU53Em02cd84AUgda2XSwAg3sOxE8f92e42f45bu7cbVZJbDJk5NuXHpyp/cRbtUNdr8EyLMvTPPbvzRcfPdU9bv/9Ems+3I1zSbFSKd/r+cnRaXssFrZnmA1hjuzLu3SalGzyYyreDMfFeaTOySZgsTAhSdD3G4YPCoEdyZCSQI4IYSIAvd97Vw+3l+O9umu0mDMfwpxsHDuOQOpCrLooLntFg7A4nnjmT0hnfuXFVBW03G6DLvLTWFFPRdn2jqcOmZS8OnBioDgzat1z1HDRRkpvPPDr3b43uEwY8xQLGYTDqebOIuJ+0I4XNqZoHuqXraI1C8/BKfT2NfUloU3ta9Va2Y1NhrDp747JHTCrMy4Kg7xYJp/yhfvTg2rSo4ZAaEnH5yIHjKEKoQQUeBAWS0qSNTTyRx6PwVF1dz2Uh5Pf3SA217Ka3e152LPStO2vnNJ8O2gcrJsfCVjcJfq0Oxw80mbnR3acmso6kZ+up2lZ1j4cn6Xzw8lt9Zoz/eIGzXNSKJ71SPw/TVw8Xd5vuwU19XVY0XB1Ds5Mvr2wB0SOmBRFpy4GWtv3Uf3hRPlfFJcyudHihnldBkB4eQF4bor0UMSwAkhRIQtWbWfh9/eHZBzzGzqXoqKzhYxeOVk2Vjxn0bqimSrmYzBCTw1f1KH+3l2Z7J+ZX3PE9u2JxKLHlZuL8XpWcXgdBmrQSPON5lu9uVc6oBfV5xGma0weQHFWTd3K9Gu2V7LykQLB+Nb5/td0tSMyWRBXXBd8C2zRFSQIVQhhIigJav2B8wl83K5jZ657i5i8A75dbR61Zu6oqtysmxMy7YFzffma6DVzMWZtoD9VXtrxOC+T2GxvU0PZhT0wfkbNQ3ueM9IwOvdOP7w+m5dwmxvoG2CQAvA+dfALZL3LZpJD5wQQkRIR8GbV7A9RtvjXcTwwJxxLLsr9CskfzZ3fNBks75um57F9C6mPekqk4Knv31RSK/ZmSWr9vvtE2tSvUvYGza92d6qZAsWlzP4sfPm9K5eIuykB04IISIgN7+40+ANYO7E4d26btDJ7yGSk2XjyfmT+Pnbu/16o8anJ9PocHHthHQWzxtPQVE18WaF3TP8qBQ8ecMkxqUn88CbO1vmv5kVfHPyCG6fmc2K7aUojO3A1u4tY83eMi4aNZgdxac5caaRP378Ja//YHpY7qut3PxiXtzk/7NJiDPHRuLh7tiVS5JuHbh/uvwU2Q4noDrPISciTgI4IYSIgI561lKT4xmfPoi5E4d3OC8tErz1+eW7e3C7NXEWE0/Mn+QX3HjTYPgGZd7jGx66Muh1275+8bzxLHw5vyXY23iwgoUv54ctiMvNL2b1nhMcOVVH6emmgOOdbUMWmxRj7Q72Wa3EuzVzGjwJgM3WLuWQE5ElAZwQQkTA3InDW3KetfXA1eOiLnDztWB6Zqd52kLRE7jlaFWHz0OlK0PZt03PCst7R5R1EHZlRKZ2bwLfYePgW3+SRQsxQAI4IYQII+/+lm0DHW+SXN/AQQH3XjEmqoM3r3AO1XpNyx7itxhiWnb7W0n1VEFRdafBm9ViYvG84KlXYlrZ54x2GPvdmr1pUrIuk+AtRkgAJ4QQYZKbX8wj7+7BrTXxFlPQhQXpg6zYkuKZkmXzG2oU8PoPprPw5XzyCisZMTiRH10d+mSySzcc7vSc1IGBW2r1C+lfYWbpJ/zZBhOa7UZy4Mm3RrpWootkFaoQQoRBQVE1v3hnN063xq2hyeH2CxYWLd/B0o2FlNU0s7+slmSrRYK3IH509fmYTIriqoYOkxP31JGK+k7Puf/KsSF9z6iRMIgJzQ7m1dXzeEUV5CyU3rcYIj1wQggRQgVF1azYXsqqz0+07KHptXZfObn5xRRX1vPOzuN+x9bsLeufw3S9lFdYSZPDWCnZ5DCSE4cy0B2dOiBgb1irRRFvNpGSGMd/XTk2Joa0eyT7cuIsCfym4jSY42W3hRgjAZwQQoRIQVE1t77wWUv6jGCeXXeQY0FWOV47IT2cVYtZB8tr/Z7/7bOjId3g/r6vncu/vyjH5W5Nd9JvA7a2giUCFjFDAjghhAiRvMLKDoM3IGjwFmdW0vvWjrZbaJ2oaeY/lm7miRsmse7ASU7WNJHkbOKJgvWgFN+/bHTQAKztYpJFy3fwz13H8f1xmU2KcenJYb6jKDNqmgRuMUoCOCGECJHaRkePXndOsjXENek/Rg1JoqzGf29Vt4aH397d5kxjLpu33DeIW7R8R8CQdTDe/U5lLqKIBRLACSFEiOw9UdOj1/XbSfIhsHjueG56bnO3XvPEB/sYl57MgbJafrNmP2ca29kuKoio2+9UiHZIACeEECHSXrJ+RfuBwX0xkvctkpQC3Y3IqsHu6nbQ5xWV+50KEYSkERFCiBDIzS/2SzrrlRBnYvzw4POqxqcny9y3TuQVVnYreOuNp9psCSZENJMeOCGECIFn1x8KKJuckcIj35zAgbLaIHO24In5k/qiajFtxpihxJtVp4tDemJQgpkZY1IZlmyVJMoi5kgAJ4QQHcjNL+aVTwo7XOEIUF1vDyh75JsTyMmykVdY6TeMOnJwAv976xQJGLogJ8vGG/fMZMX2Ut7bcYw6uyvgnCQLDBmYEHQT+rbuu2IMyYlxIU1FIkQkSAAnhBDtyM0v9us5e/jt3Tz5wT5un5HVMvQZLB0FGMOj3gBhxpihWONMOJxu4iwmCd66ybvv6k1TMgLmtiXFm/nz1xOYNWsWufnF7S5ayB6axNPfvkjaXfQbEsAJIUQb3pxhz60LHBatt7tYurGQpRsLMSsCAjcv3+HRnCwby+6aEXRTe9F1OVk2brhohF9KkIUzsoBywEgdsmB6ZsvPr7bRwd4TNcydOFwWioh+RwI4IYTwUVBUza0v5mF3ujs9t73gLTHOFBCkeXuRRO/84ZaLSR+UwJq9ZVw7IZ3F88azfn253znS1uJsIAGcEEL4WLm9tEvBW0eukW2xwmrxvPH8xesEAAAMxUlEQVSyelec9SSNiBBC+DhV29z5SR24KCOFP9xycYhqI4QQwUkPnBBCeOTmF7PuwMkev/6+K8ZIz5AQok9IACeEEASuOG3rtumZ3DglgwNltby5tZhdpWf8jg9JipPgTQjRZySAE0Kc9XLzi/l5O8Hb8EFW/nRbTsuk+JwsGwumZwZskP7tqaP6pK5CCAESwAkh+ilvTrDaRifWOBMjbUktiXi9yXlLTzfS5Gh/wYLVrPjs4auDHgu2GlIIIfqKBHARtGj5Dj7cW0ZygoVFV4+TPEVCdMFbXzTz2NZ1AUHTouU7WP/lKQbGmwMy8jc63Bw6WcfDb+/ucJi0rTsvG93hcVkNKYSIFAngIuSGP33CTs8cmkaHnYff3s2v/rmXR785gXHpyTy0oZ6Taz4AQAEmE1iUwmQycc2ENFnlJs4qBUXVPPDmToqqGjwlzpZkum2dbnD0+v1MCu65XBYkCCGilwRwEbBo+Y6W4M1Xs9MdtHdAAy43uNDgcvHOzuN8eqiCrb+YzZJV+3s9hOPNWi4Z4oUvb9BUUtWAxaywWkw02F2YTQqX1rjdkJIUx0PXXNDj3uOOtj6KlGED49n6i9mRroYQQnRIArg+lptf7DfxuadO1dnJXvxBy/O2vREJcSa+NzMbgFc/PYJTw2XnDuX1H0xvqceTH+yjPsjG0D1lVjDAamHBtEzpuYhhvr3DXnaXxu4yPisun+0HTjc4uj0sGa3MCr45eYT0bgshYoIEcH1oyar9QYd8wqHJ4Q54r40HK/yCvlBzaahpan9oq6eyt67r8ibUoeiRDCY3v5jVe04wd+JwgJbHPe15WrR8B+/tPE638/2vCd/Prz8yK0hJjKOqnWHVBIuJr2Sk8LO546X3WQgRU2I+gFNKXQv8ETADL2mtl0SyPktW7efVT4/Q3N4miaLbjlY2cNNzm7v1mlAHkV6bDlb4Pe4PPU/9iVmB1pDWJvVHQVE1SzccZkdxNW4N387JkF5iIURMi+kATillBp4FZgOlwFal1Hta632RqE9PetiGDYwn3mLiWJtVcwBXjE3l2onDeeWTQk7VN+NwahpCOOQpRCwxKfjqealcMaSO/NqUbgVjOVk2Xlw4tY9qKoQQ4RfTARwwDTiktS4EUEotB64HIhLAvbPzWLfO950svWTVfpblF6GUYsG0TGYklTNrljFfre0wXds5SgpjoYMQoWZSMCIlgap6Ow63JtlqocnhwmI2kZZspbCint52NltMiuu+MrzLc8/Wr1/PXfMlGBNCnN2U1rH7X79S6mbgWq31XZ7ntwPTtdY/bHPePcA9AGlpaTnLly8PS32eym/ky+quzWpKtcLvrhzQ7vG6ujoGDhzYo3q89UUza4ucODw/2ngTWM1Q22YakEXBnCwL377A2qP3WV/sYFu5k8xkE2X1bg6fdlProPvzujqkMUJU0VdtoYAJQ0z8ZFpi2N+rJ3rzb6O/kbYwSDu0krYw9Kd2uPLKKwu01gF/tcZ6D1yXaK1fAF4AmDp1qp41a1ZY3id5dDXffn4zrg4imGSrmf+ed2Gnk9/Xr19PT+sZptsLfJ8+eI/rfruavVXubvUwxkqPZPbQpC4vzoDefSb6E2mHVtIWBmmHVtIWhrOhHWI9gDsG+G5AmOEpi4icLBtv3Xup5FQLoZ9MS+z3/wiFEEKI7or1AG4rMFYpNRojcLsFWBDJCuVk2SRwE0IIIURYxXQAp7V2KqV+CHyIkUbkFa313ghXSwghhBAirGI6gAPQWq8CVkW6HkIIIYQQfcUU6QoIIYQQQojukQBOCCGEECLGSAAnhBBCCBFjJIATQgghhIgxEsAJIYQQQsQYCeCEEEIIIWKMBHBCCCGEEDFGAjghhBBCiBgjAZwQQgghRIyRAE4IIYQQIsZIACeEEEIIEWOU1jrSdehTSqlTQFGk69EFqUBFpCsRBaQdWklbGKQdWklbGKQdWklbGPpTO2RprYe1LTzrArhYoZTaprWeGul6RJq0QytpC4O0QytpC4O0QytpC8PZ0A4yhCqEEEIIEWMkgBNCCCGEiDESwEWvFyJdgSgh7dBK2sIg7dBK2sIg7dBK2sLQ79tB5sAJIYQQQsQY6YETQgghhIgxEsAJIYQQQsQYCeD6iFJqlFJqnVJqn1Jqr1LqR57yIUqptUqpg57vNk/5BUqpz5RSzUqpnwS5nlkptUMp9X5f30tvhLIdlFJHlVK7lVI7lVLbInE/vRHithislPqHUuoLpdR+pdTMSNxTT4SqHZRS4zyfBe9XjVJqUaTuqydC/Jn4secae5RSbyilEiJxTz0R4nb4kacN9sba5wF61Ba3KaU+9/xu3KyUmuxzrWuVUgeUUoeUUosjdU89EeJ2eEUpdVIptSdS9xMSWmv56oMvYDgwxfM4GfgSuBD4LbDYU74Y+I3n8TnAJcCTwE+CXO8BIBd4P9L3Fql2AI4CqZG+pyhpi78Ad3kexwODI31/kWgHn2uagTKMBJgRv8e+bgtgJHAESPQ8fwv4XqTvLwLtMBHYAyQBFuBj4LxI31+Y2+JSwOZ5PBfI9zw2A4eBMZ7fEbuACyN9f33dDp7nVwBTgD2Rvq/efEkPXB/RWp/QWm/3PK4F9mP8kr0e4z9fPN9v8JxzUmu9FXC0vZZSKgP4BvBSH1Q9pELZDrEuVG2hlErB+IX0suc8u9b6dJ/cRAiE6TNxFXBYax0Lu660CHFbWIBEpZQFI4A5Hubqh0wI22E8xn/cDVprJ7ABuLEPbiFketAWm7XW1Z7yPCDD83gacEhrXai1tgPLPdeICSFsB7TWG4GqPqp62EgAFwFKqWzgYiAfSNNan/AcKgPSunCJPwAPAe5w1K+vhKAdNPCRUqpAKXVPWCrZR3rZFqOBU8CryhhWf0kpNSBcdQ2nEHwmvG4B3ghp5fpYb9pCa30M+B1QDJwAzmitPwpbZcOol5+JPcDlSqmhSqkkYB4wKkxVDbsetMUPgNWexyOBEp9jpZ6ymNPLdug3JIDrY0qpgcAKYJHWusb3mDb6djvM66KUug44qbUuCF8tw6+37eDxVa31FIzu8fuVUleEvqbhF4K2sGAMBzyntb4YqMcYSogpIfpMoJSKB74F/D3klewjIfg9YcPomRgNjAAGKKW+G6bqhk1v20FrvR/4DfARsAbYCbjCU9vw6m5bKKWuxAhcftZnlewD0g6tJIDrQ0qpOIwP3jKt9UpPcblSarjn+HDgZCeXuQz4llLqKEYX+NeVUn8LU5XDIkTt4O1lQGt9EngbY4ggpoSoLUqBUq11vuf5PzACupgRqs+Ex1xgu9a6PPQ1Db8QtcXVwBGt9SmttQNYiTEnKGaE8PfEy1rrHK31FUA1xtypmNLdtlBKfQVjis31WutKT/Ex/HsfMzxlMSNE7dBvSADXR5RSCmOO0n6t9e99Dr0H3OF5fAfwbkfX0Vr/t9Y6Q2udjTFM9G+tdcz8ZR2qdlBKDVBKJXsfA3MwhktiRgg/E2VAiVJqnKfoKmBfiKsbNqFqBx+3EqPDpyFsi2JghlIqyXPNqzDmDMWEUH4mlFLneL5nYsx/yw1tbcOru23huc+VwO1aa99gdSswVik12tNLfYvnGjEhhO3Qf3S0wkG+QvcFfBWja/dzjG78nRjzMYYC/wIOYqyQGuI5Px2jZ6UGOO15PKjNNWcRe6tQQ9IOGCupdnm+9gI/j/S9RfIzAVwEbPNc6x08q69i4SvE7TAAqARSIn1fUdAWvwK+wPjD5q+ANdL3F6F22ITxB80u4KpI31sftMVLGD2N3nO3+VxrHkYP5OFY+50Z4nZ4A2NuqMPzWflBpO+vJ1+ylZYQQgghRIyRIVQhhBBCiBgjAZwQQgghRIyRAE4IIYQQIsZIACeEEEIIEWMkgBNCCCGEiDESwAkhRBuerZd2er7KlFLHPI/rlFJ/jnT9hBBC0ogIIUQHlFKPAXVa699Fui5CCOElPXBCCNFFSqlZSqn3PY8fU0r9RSm1SSlVpJS6USn1W6XUbqXUGs+2PyilcpRSG5RSBUqpD73b/gghRG9IACeEED13LvB14FvA34B1WutJQCPwDU8Q93/AzVrrHOAV4MlIVVYI0X9YIl0BIYSIYau11g6l1G7ADKzxlO8GsoFxwERgrbGVI2aMLXyEEKJXJIATQoieawbQWruVUg7dOqnYjfH7VQF7tdYzI1VBIUT/JEOoQggRPgeAYUqpmQBKqTil1IQI10kI0Q9IACeEEGGitbYDNwO/UUrtAnYCl0a2VkKI/kDSiAghhBBCxBjpgRNCCCGEiDESwAkhhBBCxBgJ4IQQQgghYowEcEIIIYQQMUYCOCGEEEKIGCMBnBBCCCFEjJEATgghhBAixvx/Ki0BTMsiSA4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Zj2p8NAkRLD"
      },
      "source": [
        "The naive forecast looks like it's following the data well. \n",
        "\n",
        "Let's zoom in to take a better look.\n",
        "\n",
        "We can do so by creating an offset value and passing it to the `start` parameter of our `plot_time_series()` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "dx0wUhtOW_VX",
        "outputId": "ef5eb197-0ce7-486d-f5e6-7745bb302b68"
      },
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "offset = 300 # offset the values by 300 timesteps \n",
        "plot_time_series(timesteps=X_test, values=y_test, start=offset, label=\"Test data\")\n",
        "plot_time_series(timesteps=X_test[1:], values=naive_forecast, format=\"-\", start=offset, label=\"Naive forecast\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdPsP9SwD72K"
      },
      "source": [
        "When we zoom in we see the naïve forecast comes slightly after the test data. This makes sense because the naive forecast uses the previous timestep value to predict the next timestep value.\n",
        "\n",
        "Forecast made. Time to evaluate it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdK4KoFnA90I"
      },
      "source": [
        "## Evaluating a time series model\n",
        "\n",
        "Time series forecasting often involves predicting a number (in our case, the price of Bitcoin).\n",
        "\n",
        "And what kind of problem is predicting a number?\n",
        "\n",
        "Ten points if you said regression.\n",
        "\n",
        "With this known, we can use regression evaluation metrics to evaluate our time series forecasts.\n",
        "\n",
        "The main thing we will be evaluating is: **how do our model's predictions (`y_pred`) compare against the actual values (`y_true` or *ground truth values*)**? \n",
        "\n",
        "> 📖 **Resource:** We're going to be using several metrics to evaluate our different model's time series forecast accuracy. Many of them are sourced and explained mathematically and conceptually in [Forecasting: Principles and Practice chapter 5.8](https://otexts.com/fpp3/accuracy.html), I'd recommend reading through here for a more in-depth overview of what we're going to practice.\n",
        "\n",
        "For all of the following metrics, **lower is better** (for example an MAE of 0 is better than an MAE 100).\n",
        "\n",
        "### Scale-dependent errors\n",
        "\n",
        "These are metrics which can be used to compare time series values and forecasts that are on the same scale.\n",
        "\n",
        "For example, Bitcoin historical prices in USD veresus Bitcoin forecast values in USD.\n",
        "\n",
        "| Metric | Details | Code |\n",
        "| ----- | ----- | ----- |\n",
        "| **MAE** (mean absolute error) | Easy to interpret (a forecast is X amount different from actual amount). Forecast methods which minimises the MAE will lead to forecasts of the median. | [`tf.keras.metrics.mean_absolute_error()`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/MeanAbsoluteError) |\n",
        "| **RMSE** (root mean square error) | Forecasts which minimise the RMSE lead to forecasts of the mean. | `tf.sqrt(`[`tf.keras.metrics.mean_square_error()`](https://www.tensorflow.org/api_docs/python/tf/keras/metrics/RootMeanSquaredError)`)`|\n",
        "\n",
        "### Percentage errors\n",
        "\n",
        "Percentage errors do not have units, this means they can be used to compare forecasts across different datasets.\n",
        "\n",
        "| **Metric** | **Details** | **Code** |\n",
        "| ----- | ----- | ----- |\n",
        "| **MAPE** (mean absolute percentage error) | Most commonly used percentage error. May explode (not work) if `y=0`. | [`tf.keras.metrics.mean_absolute_percentage_error()`](https://www.tensorflow.org/api_docs/python/tf/keras/losses/MAPE) | \n",
        "| **sMAPE** (symmetric mean absolute percentage error) | Recommended not to be used by [Forecasting: Principles and Practice](https://otexts.com/fpp3/accuracy.html#percentage-errors), though it is used in forecasting competitions. | Custom implementation |\n",
        "\n",
        "### Scaled errors\n",
        "\n",
        "Scaled errors are an alternative to percentage errors when comparing forecast performance across different time series.\n",
        "\n",
        "| **Metric** | **Details** | **Code** |\n",
        "| ----- | ----- | ----- |\n",
        "| **MASE** (mean absolute scaled error). | MASE equals one for the naive forecast (or very close to one). A forecast which performs better than the naïve should get <1 MASE. | See sktime's [`mase_loss()`](https://github.com/alan-turing-institute/sktime/blob/ee7a06843a44f4aaec7582d847e36073a9ab0566/sktime/performance_metrics/forecasting/_functions.py#L16) |\n",
        "\n",
        "> 🤔 **Question:** There are so many metrics... which one should I pay most attention to? It's going to depend on your problem. However, since its ease of interpretation (you can explain it in a sentence to your grandma), MAE is often a very good place to start.\n",
        "\n",
        "Since we're going to be evaluing a lot of models, let's write a function to help us calculate evaluation metrics on their forecasts.\n",
        "\n",
        "First we'll need TensorFlow.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljaO69huYCZD"
      },
      "source": [
        "# Let's get TensorFlow! \n",
        "import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ys3PAVUTo6q"
      },
      "source": [
        "And since TensorFlow doesn't have a ready made version of MASE (mean aboslute scaled error), how about we create our own?\n",
        "\n",
        "We'll take inspiration from [sktime](https://github.com/alan-turing-institute/sktime)'s (Scikit-Learn for time series) [`MeanAbsoluteScaledError`](https://www.sktime.org/en/stable/api_reference/auto_generated/sktime.performance_metrics.forecasting.MeanAbsoluteScaledError.html#sktime.performance_metrics.forecasting.MeanAbsoluteScaledError) class which calculates the MASE."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jbLBJ_IGjla"
      },
      "source": [
        "# MASE implemented courtesy of sktime - https://github.com/alan-turing-institute/sktime/blob/ee7a06843a44f4aaec7582d847e36073a9ab0566/sktime/performance_metrics/forecasting/_functions.py#L16\n",
        "def mean_absolute_scaled_error(y_true, y_pred):\n",
        "  \"\"\"\n",
        "  Implement MASE (assuming no seasonality of data).\n",
        "  \"\"\"\n",
        "  mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n",
        "\n",
        "  # Find MAE of naive forecast (no seasonality)\n",
        "  mae_naive_no_season = tf.reduce_mean(tf.abs(y_true[1:] - y_true[:-1])) # our seasonality is 1 day (hence the shifting of 1 day)\n",
        "\n",
        "  return mae / mae_naive_no_season"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yD4P7-wwUVWj"
      },
      "source": [
        "You'll notice the version of MASE above doesn't take in the training values like sktime's `mae_loss()`. In our case, we're comparing the MAE of our predictions on the test to the MAE of the naïve forecast on the test set.\n",
        "\n",
        "In practice, if we've created the function correctly, the naïve model should achieve an MASE of 1 (or very close to 1). Any model worse than the naïve forecast will achieve an MASE of >1 and any model better than the naïve forecast will achieve an MASE of <1.\n",
        "\n",
        "Let's put each of our different evaluation metrics together into a function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwMeVgRXWaJp"
      },
      "source": [
        "def evaluate_preds(y_true, y_pred):\n",
        "  # Make sure float32 (for metric calculations)\n",
        "  y_true = tf.cast(y_true, dtype=tf.float32)\n",
        "  y_pred = tf.cast(y_pred, dtype=tf.float32)\n",
        "\n",
        "  # Calculate various metrics\n",
        "  mae = tf.keras.metrics.mean_absolute_error(y_true, y_pred)\n",
        "  mse = tf.keras.metrics.mean_squared_error(y_true, y_pred) # puts and emphasis on outliers (all errors get squared)\n",
        "  rmse = tf.sqrt(mse)\n",
        "  mape = tf.keras.metrics.mean_absolute_percentage_error(y_true, y_pred)\n",
        "  mase = mean_absolute_scaled_error(y_true, y_pred)\n",
        "  \n",
        "  return {\"mae\": mae.numpy(),\n",
        "          \"mse\": mse.numpy(),\n",
        "          \"rmse\": rmse.numpy(),\n",
        "          \"mape\": mape.numpy(),\n",
        "          \"mase\": mase.numpy()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VAKTXEIPVKfR"
      },
      "source": [
        "Looking good! How about we test our function on the naive forecast?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbJlsvUwYEsf",
        "outputId": "3b2c1d91-dfd4-4266-9927-4f0965613656"
      },
      "source": [
        "naive_results = evaluate_preds(y_true=y_test[1:],\n",
        "                               y_pred=naive_forecast)\n",
        "naive_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 567.9802,\n",
              " 'mape': 2.516525,\n",
              " 'mase': 0.99957,\n",
              " 'mse': 1147547.0,\n",
              " 'rmse': 1071.2362}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bXUPHswVSjp"
      },
      "source": [
        "Alright, looks like we've got some baselines to beat.\n",
        "\n",
        "Taking a look at the naïve forecast's MAE, it seems on average each forecast is ~$567 different than the actual Bitcoin price.\n",
        "\n",
        "How does this compare to the average price of Bitcoin in the test dataset?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yjl-X9QLVhp1",
        "outputId": "d96d84d8-af33-4574-98c6-cecba5a211a9"
      },
      "source": [
        "# Find average price of Bitcoin in test dataset\n",
        "tf.reduce_mean(y_test).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20056.632963737226"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpEk5iOZWD1o"
      },
      "source": [
        "Okay, looking at these two values is starting to give us an idea of how our model is performing:\n",
        "* The average price of Bitcoin in the test dataset is: $20,056 (note: average may not be the best measure here, since the highest price is over 3x this value and the lowest price is over 4x lower)\n",
        "\n",
        "* Each prediction in naive forecast is on average off by: $567\n",
        "\n",
        "Is this enough to say it's a good model?\n",
        "\n",
        "That's up your own interpretation. Personally, I'd prefer a model which was closer to the mark.\n",
        "\n",
        "How about we try and build one?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5kgVzdq1S1A"
      },
      "source": [
        "## Other kinds of time series forecasting models which can be used for baselines and actual forecasts\n",
        "\n",
        "Since we've got a naïve forecast baseline to work with, it's time we start building models to try and beat it.\n",
        "\n",
        "And because this course is focused on TensorFlow and deep learning, we're going to be using TensorFlow to build deep learning models to try and improve on our naïve forecasting results.\n",
        "\n",
        "That being said, there are many other kinds of models you may want to look into for building baselines/performing forecasts.\n",
        "\n",
        "Some of them may even beat our best performing models in this notebook, however, I'll leave trying them out for extra-curriculum.\n",
        "\n",
        "| **Model/Library Name** | **Resource** |\n",
        "| ----- | ----- |\n",
        "| Moving average | https://machinelearningmastery.com/moving-average-smoothing-for-time-series-forecasting-python/ |\n",
        "| ARIMA (Autoregression Integrated Moving Average) | https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/ |\n",
        "| sktime (Scikit-Learn for time series) | https://github.com/alan-turing-institute/sktime |\n",
        "| TensorFlow Decision Forests (random forest, gradient boosting trees) | https://www.tensorflow.org/decision_forests |\n",
        "| Facebook Kats (purpose-built forecasting and time series analysis library by Facebook) | https://github.com/facebookresearch/Kats | \n",
        "| LinkedIn Greykite (flexible, intuitive and fast forecasts) | https://github.com/linkedin/greykite |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8AzwUvjBkgY"
      },
      "source": [
        "## Format Data Part 2: Windowing dataset\n",
        "\n",
        "Surely we'd be ready to start building models by now?\n",
        "\n",
        "We're so close! Only one more step (really two) to go.\n",
        "\n",
        "We've got to window our time series.\n",
        "\n",
        "Why do we window?\n",
        "\n",
        "Windowing is a method to turn a time series dataset into **supervised learning problem**. \n",
        "\n",
        "In other words, we want to use windows of the past to predict the future.\n",
        "\n",
        "For example for a univariate time series, windowing for one week (`window=7`) to predict the next single value (`horizon=1`) might look like:\n",
        "\n",
        "```\n",
        "Window for one week (univariate time series)\n",
        "\n",
        "[0, 1, 2, 3, 4, 5, 6] -> [7]\n",
        "[1, 2, 3, 4, 5, 6, 7] -> [8]\n",
        "[2, 3, 4, 5, 6, 7, 8] -> [9]\n",
        "```\n",
        "\n",
        "Or for the price of Bitcoin, it'd look like:\n",
        "\n",
        "```\n",
        "Window for one week with the target of predicting the next day (Bitcoin prices)\n",
        "\n",
        "[123.654, 125.455, 108.584, 118.674, 121.338, 120.655, 121.795] -> [123.033]\n",
        "[125.455, 108.584, 118.674, 121.338, 120.655, 121.795, 123.033] -> [124.049]\n",
        "[108.584, 118.674, 121.338, 120.655, 121.795, 123.033, 124.049] -> [125.961]\n",
        "```\n",
        "\n",
        "![windows and horizons for turning time series data into a supervised learning problem](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-time-series-windows-and-horizons.png)\n",
        "*Example of windows and horizons for Bitcoin data. Windowing can be used to turn time series data into a supervised learning problem.*\n",
        "\n",
        "Let's build some functions which take in a univariate time series and turn it into windows and horizons of specified sizes.\n",
        "\n",
        "We'll start with the default horizon size of 1 and a window size of 7 (these aren't necessarily the best values to use, I've just picked them)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LibzWQXM2ib-"
      },
      "source": [
        "HORIZON = 1 # predict 1 step at a time\n",
        "WINDOW_SIZE = 7 # use a week worth of timesteps to predict the horizon"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AQ0HWWzfRW_"
      },
      "source": [
        "Now we'll write a function to take in an array and turn it into a window and horizon."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zV8pv5K_x81E"
      },
      "source": [
        "# Create function to label windowed data\n",
        "def get_labelled_windows(x, horizon=1):\n",
        "  \"\"\"\n",
        "  Creates labels for windowed dataset.\n",
        "\n",
        "  E.g. if horizon=1 (default)\n",
        "  Input: [1, 2, 3, 4, 5, 6] -> Output: ([1, 2, 3, 4, 5], [6])\n",
        "  \"\"\"\n",
        "  return x[:, :-horizon], x[:, -horizon:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejJ9vCyKff8F",
        "outputId": "805b4459-9f5e-4311-9592-41efefd22546"
      },
      "source": [
        "# Test out the window labelling function\n",
        "test_window, test_label = get_labelled_windows(tf.expand_dims(tf.range(8)+1, axis=0), horizon=HORIZON)\n",
        "print(f\"Window: {tf.squeeze(test_window).numpy()} -> Label: {tf.squeeze(test_label).numpy()}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [1 2 3 4 5 6 7] -> Label: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qc4U4ExKfx6S"
      },
      "source": [
        "Oh yeah, that's what I'm talking about!\n",
        "\n",
        "Now we need a way to make windows for an entire time series.\n",
        "\n",
        "We could do this with Python for loops, however, for large time series, that'd be quite slow.\n",
        "\n",
        "To speed things up, we'll leverage [NumPy's array indexing](https://numpy.org/doc/stable/reference/arrays.indexing.html).\n",
        "\n",
        "Let's write a function which:\n",
        "1. Creates a window step of specific window size, for example: `[[0, 1, 2, 3, 4, 5, 6, 7]]`\n",
        "2. Uses NumPy indexing to create a 2D of multiple window steps, for example: \n",
        "```\n",
        "[[0, 1, 2, 3, 4, 5, 6, 7],\n",
        " [1, 2, 3, 4, 5, 6, 7, 8],\n",
        " [2, 3, 4, 5, 6, 7, 8, 9]]\n",
        "```\n",
        "3. Uses the 2D array of multuple window steps to index on a target series\n",
        "4. Uses the `get_labelled_windows()` function we created above to turn the window steps into windows with a specified horizon\n",
        "\n",
        "> 📖 **Resource:** The function created below has been adapted from Syafiq Kamarul Azman's article [*Fast and Robust Sliding Window Vectorization with NumPy*](https://towardsdatascience.com/fast-and-robust-sliding-window-vectorization-with-numpy-3ad950ed62f5)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apea0wiJyCI6"
      },
      "source": [
        "# Create function to view NumPy arrays as windows \n",
        "def make_windows(x, window_size=7, horizon=1):\n",
        "  \"\"\"\n",
        "  Turns a 1D array into a 2D array of sequential windows of window_size.\n",
        "  \"\"\"\n",
        "  # 1. Create a window of specific window_size (add the horizon on the end for later labelling)\n",
        "  window_step = np.expand_dims(np.arange(window_size+horizon), axis=0)\n",
        "  # print(f\"Window step:\\n {window_step}\")\n",
        "\n",
        "  # 2. Create a 2D array of multiple window steps (minus 1 to account for 0 indexing)\n",
        "  window_indexes = window_step + np.expand_dims(np.arange(len(x)-(window_size+horizon-1)), axis=0).T # create 2D array of windows of size window_size\n",
        "  # print(f\"Window indexes:\\n {window_indexes[:3], window_indexes[-3:], window_indexes.shape}\")\n",
        "\n",
        "  # 3. Index on the target array (time series) with 2D array of multiple window steps\n",
        "  windowed_array = x[window_indexes]\n",
        "\n",
        "  # 4. Get the labelled windows\n",
        "  windows, labels = get_labelled_windows(windowed_array, horizon=horizon)\n",
        "\n",
        "  return windows, labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYQD0PRLkMIs"
      },
      "source": [
        "Phew! A few steps there... let's see how it goes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7Yc3V7fc1UG",
        "outputId": "41dce6b2-d01b-4943-f1c8-a113c256bc2d"
      },
      "source": [
        "full_windows, full_labels = make_windows(prices, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(full_windows), len(full_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2780, 2780)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhj521fWlJsy"
      },
      "source": [
        "Of course we have to visualize, visualize, visualize!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1P-MT4CkYta",
        "outputId": "a00a6bb8-de3e-4861-84bc-442441b09635"
      },
      "source": [
        "# View the first 3 windows/labels\n",
        "for i in range(3):\n",
        "  print(f\"Window: {full_windows[i]} -> Label: {full_labels[i]}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [123.65499 125.455   108.58483 118.67466 121.33866 120.65533 121.795  ] -> Label: [123.033]\n",
            "Window: [125.455   108.58483 118.67466 121.33866 120.65533 121.795   123.033  ] -> Label: [124.049]\n",
            "Window: [108.58483 118.67466 121.33866 120.65533 121.795   123.033   124.049  ] -> Label: [125.96116]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCDtm6gskjRH",
        "outputId": "0d02d8c6-d0af-45b6-e98f-bbf22172f572"
      },
      "source": [
        "# View the last 3 windows/labels\n",
        "for i in range(3):\n",
        "  print(f\"Window: {full_windows[i-3]} -> Label: {full_labels[i-3]}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [58788.20967893 58102.19142623 55715.54665129 56573.5554719\n",
            " 52147.82118698 49764.1320816  50032.69313676] -> Label: [47885.62525472]\n",
            "Window: [58102.19142623 55715.54665129 56573.5554719  52147.82118698\n",
            " 49764.1320816  50032.69313676 47885.62525472] -> Label: [45604.61575361]\n",
            "Window: [55715.54665129 56573.5554719  52147.82118698 49764.1320816\n",
            " 50032.69313676 47885.62525472 45604.61575361] -> Label: [43144.47129086]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "exTivzXvgbmb"
      },
      "source": [
        "> 🔑 **Note:** You can find a function which achieves similar results to the ones we implemented above at [`tf.keras.preprocessing.timeseries_dataset_from_array()`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/timeseries_dataset_from_array). Just like ours, it takes in an array and returns a windowed dataset. It has the benefit of returning data in the form of a tf.data.Dataset instance (we'll see how to do this with our own data later).\n",
        "\n",
        "## Turning windows into training and test sets \n",
        "\n",
        "Look how good those windows look! Almost like the stain glass windows on the Sistine Chapel, well, maybe not that good but still.\n",
        "\n",
        "Time to turn our windows into training and test splits.\n",
        "\n",
        "We could've windowed our existing training and test splits, however, with the nature of windowing (windowing often requires an offset at some point in the data), it usually works better to window the data first, then split it into training and test sets.\n",
        "\n",
        "Let's write a function which takes in full sets of windows and their labels and splits them into train and test splits."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_8BRgb8YtP3"
      },
      "source": [
        "# Make the train/test splits\n",
        "def make_train_test_splits(windows, labels, test_split=0.2):\n",
        "  \"\"\"\n",
        "  Splits matching pairs of windows and labels into train and test splits.\n",
        "  \"\"\"\n",
        "  split_size = int(len(windows) * (1-test_split)) # this will default to 80% train/20% test\n",
        "  train_windows = windows[:split_size]\n",
        "  train_labels = labels[:split_size]\n",
        "  test_windows = windows[split_size:]\n",
        "  test_labels = labels[split_size:]\n",
        "  return train_windows, test_windows, train_labels, test_labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_062e8Oec9cf"
      },
      "source": [
        "Look at that amazing function, lets test it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_xY8T7HEaoU",
        "outputId": "d31a4535-f5b7-4ee6-92bd-a056a0101794"
      },
      "source": [
        "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(full_windows, full_labels)\n",
        "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2224, 556, 2224, 556)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75_YBwiedI2s"
      },
      "source": [
        "Notice the default split of 80% training data and 20% testing data (this split can be adjusted if needed).\n",
        "\n",
        "How do the first 5 samples of the training windows and labels looks?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDgyT7Gix1fH",
        "outputId": "ca96d4e6-56f0-4355-d2bb-6ad981fc3dae"
      },
      "source": [
        "train_windows[:5], train_labels[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[123.65499, 125.455  , 108.58483, 118.67466, 121.33866, 120.65533,\n",
              "         121.795  ],\n",
              "        [125.455  , 108.58483, 118.67466, 121.33866, 120.65533, 121.795  ,\n",
              "         123.033  ],\n",
              "        [108.58483, 118.67466, 121.33866, 120.65533, 121.795  , 123.033  ,\n",
              "         124.049  ],\n",
              "        [118.67466, 121.33866, 120.65533, 121.795  , 123.033  , 124.049  ,\n",
              "         125.96116],\n",
              "        [121.33866, 120.65533, 121.795  , 123.033  , 124.049  , 125.96116,\n",
              "         125.27966]]), array([[123.033  ],\n",
              "        [124.049  ],\n",
              "        [125.96116],\n",
              "        [125.27966],\n",
              "        [125.9275 ]]))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkF0HmvadHBB",
        "outputId": "a257bf12-cd34-4843-e517-fc6863fcdd15"
      },
      "source": [
        "# Check to see if same (accounting for horizon and window size)\n",
        "np.array_equal(np.squeeze(train_labels[:-HORIZON-1]), y_train[WINDOW_SIZE:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQX0PF5jV4aq"
      },
      "source": [
        "## Make a modelling checkpoint\n",
        "\n",
        "We're so close to building models. So so so close.\n",
        "\n",
        "Because our model's performance will fluctuate from experiment to experiment, we'll want to make sure we're comparing apples to apples.\n",
        "\n",
        "What I mean by this is in order for a fair comparison, we want to compare each model's best performance against each model's best performance.\n",
        "\n",
        "For example, if `model_1` performed incredibly well on epoch 55 but its performance fell off toward epoch 100, we want the version of the model from epoch 55 to compare to other models rather than the version of the model from epoch 100.\n",
        "\n",
        "And the same goes for each of our other models: compare the best against the best.\n",
        "\n",
        "To take of this, we'll implement a [`ModelCheckpoint`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint) callback.\n",
        "\n",
        "The `ModelCheckpoint` callback will monitor our model's performance during training and save the best model to file by setting `save_best_only=True`.\n",
        "\n",
        "That way when evaluating our model we could restore its best performing configuration from file.\n",
        "\n",
        "> 🔑 **Note:** Because of the size of the dataset (smaller than usual), you'll notice our modelling experiment results fluctuate quite a bit during training (hence the implementation of the `ModelCheckpoint` callback to save the best model).\n",
        "\n",
        "Because we're going to be running multiple experiments, it makes sense to keep track of them by saving models to file under different names.\n",
        "\n",
        "To do this, we'll write a small function to create a `ModelCheckpoint` callback which saves a model to specified filename."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xh8i1f7rV_P2"
      },
      "source": [
        "import os\n",
        "\n",
        "# Create a function to implement a ModelCheckpoint callback with a specific filename \n",
        "def create_model_checkpoint(model_name, save_path=\"model_experiments\"):\n",
        "  return tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(save_path, model_name), # create filepath to save model\n",
        "                                            verbose=0, # only output a limited amount of text\n",
        "                                            save_best_only=True) # save only the best model to file"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5uw1AaVY-J9"
      },
      "source": [
        "## Model 1: Dense model (window = 7, horizon = 1)\n",
        "\n",
        "Finally!\n",
        "\n",
        "Time to build one of our models.\n",
        "\n",
        "If you think we've been through a fair bit of preprocessing before getting here, you're right.\n",
        "\n",
        "Often, preparing data for a model is one of the largest parts of any machine learning project.\n",
        "\n",
        "And once you've got a good model in place, you'll probably notice far more improvements from manipulating the data (e.g. collecting more, improving the quality) than manipulating the model.\n",
        "\n",
        "We're going to start by keeping it simple, `model_1` will have:\n",
        "* A single dense layer with 128 hidden units and ReLU (rectified linear unit) activation\n",
        "* An output layer with linear activation (or no activation)\n",
        "* Adam optimizer and MAE loss function\n",
        "* Batch size of 128\n",
        "* 100 epochs\n",
        "\n",
        "Why these values?\n",
        "\n",
        "I picked them out of experimentation. \n",
        "\n",
        "A batch size of 32 works pretty well too and we could always train for less epochs but since the model runs so fast (you'll see in a second, it's because the number of samples we have isn't massive) we might as well train for more.\n",
        "\n",
        "> 🔑 **Note:** As always, many of the values for machine learning problems are experimental. A reminder that the values you can set yourself in a machine learning algorithm (the hidden units, the batch size, horizon size, window size) are called [**hyperparameters**](https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning). And experimenting to find the best values for hyperparameters is called **hyperparameter tuning**. Where as parameters learned by a model itself (patterns in the data, formally called weights & biases) are referred to as **parameters**.\n",
        "\n",
        "Let's import TensorFlow and build our first deep learning model for time series."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Slr5hrJHY_Ed",
        "outputId": "95b645ae-d08a-4794-a3e6-db8d1e1e3c67"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "# Set random seed for as reproducible results as possible\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Construct model\n",
        "model_1 = tf.keras.Sequential([\n",
        "  layers.Dense(128, activation=\"relu\"),\n",
        "  layers.Dense(HORIZON, activation=\"linear\") # linear activation is the same as having no activation                        \n",
        "], name=\"model_1_dense\") # give the model a name so we can save it\n",
        "\n",
        "# Compile model\n",
        "model_1.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[\"mae\"]) # we don't necessarily need this when the loss function is already MAE\n",
        "\n",
        "# Fit model\n",
        "model_1.fit(x=train_windows, # train windows of 7 timesteps of Bitcoin prices\n",
        "            y=train_labels, # horizon value of 1 (using the previous 7 timesteps to predict next day)\n",
        "            epochs=100,\n",
        "            verbose=1,\n",
        "            batch_size=128,\n",
        "            validation_data=(test_windows, test_labels),\n",
        "            callbacks=[create_model_checkpoint(model_name=model_1.name)]) # create ModelCheckpoint callback to save best model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "18/18 [==============================] - 3s 12ms/step - loss: 780.3455 - mae: 780.3455 - val_loss: 2279.6526 - val_mae: 2279.6526\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 2/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 247.6756 - mae: 247.6756 - val_loss: 1005.9991 - val_mae: 1005.9991\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 3/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 188.4116 - mae: 188.4116 - val_loss: 923.2862 - val_mae: 923.2862\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 4/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 169.4340 - mae: 169.4340 - val_loss: 900.5872 - val_mae: 900.5872\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 5/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 165.0895 - mae: 165.0895 - val_loss: 895.2238 - val_mae: 895.2238\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 6/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 158.5210 - mae: 158.5210 - val_loss: 855.1982 - val_mae: 855.1982\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 7/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 151.3566 - mae: 151.3566 - val_loss: 840.9168 - val_mae: 840.9168\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 8/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 145.2560 - mae: 145.2560 - val_loss: 803.5956 - val_mae: 803.5956\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 9/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 144.3546 - mae: 144.3546 - val_loss: 799.5455 - val_mae: 799.5455\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 10/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 141.2943 - mae: 141.2943 - val_loss: 763.5010 - val_mae: 763.5010\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 11/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 135.6595 - mae: 135.6595 - val_loss: 771.3357 - val_mae: 771.3357\n",
            "Epoch 12/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 134.1700 - mae: 134.1700 - val_loss: 782.8079 - val_mae: 782.8079\n",
            "Epoch 13/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 134.6015 - mae: 134.6015 - val_loss: 784.4449 - val_mae: 784.4449\n",
            "Epoch 14/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 130.6127 - mae: 130.6127 - val_loss: 751.3234 - val_mae: 751.3234\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 15/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 128.8347 - mae: 128.8347 - val_loss: 696.5757 - val_mae: 696.5757\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 16/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 124.7739 - mae: 124.7739 - val_loss: 702.4698 - val_mae: 702.4698\n",
            "Epoch 17/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 123.4474 - mae: 123.4474 - val_loss: 704.9241 - val_mae: 704.9241\n",
            "Epoch 18/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 122.2105 - mae: 122.2105 - val_loss: 667.9725 - val_mae: 667.9725\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 19/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 121.7263 - mae: 121.7263 - val_loss: 718.8797 - val_mae: 718.8797\n",
            "Epoch 20/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 119.2420 - mae: 119.2420 - val_loss: 657.0667 - val_mae: 657.0667\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 21/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 121.2275 - mae: 121.2275 - val_loss: 637.0330 - val_mae: 637.0330\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 22/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 119.9544 - mae: 119.9544 - val_loss: 671.2490 - val_mae: 671.2490\n",
            "Epoch 23/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 121.9248 - mae: 121.9248 - val_loss: 633.3593 - val_mae: 633.3593\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 24/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 116.3666 - mae: 116.3666 - val_loss: 624.4852 - val_mae: 624.4852\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 25/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 114.6816 - mae: 114.6816 - val_loss: 619.7571 - val_mae: 619.7571\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 26/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 116.4455 - mae: 116.4455 - val_loss: 615.6364 - val_mae: 615.6364\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 27/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 116.5868 - mae: 116.5868 - val_loss: 615.9631 - val_mae: 615.9631\n",
            "Epoch 28/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 113.4691 - mae: 113.4691 - val_loss: 608.0920 - val_mae: 608.0920\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 29/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 113.7598 - mae: 113.7598 - val_loss: 621.9306 - val_mae: 621.9306\n",
            "Epoch 30/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 116.8613 - mae: 116.8613 - val_loss: 604.4056 - val_mae: 604.4056\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 31/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 111.9375 - mae: 111.9375 - val_loss: 609.3882 - val_mae: 609.3882\n",
            "Epoch 32/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 112.4175 - mae: 112.4175 - val_loss: 603.0588 - val_mae: 603.0588\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 33/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 112.6697 - mae: 112.6697 - val_loss: 645.6975 - val_mae: 645.6975\n",
            "Epoch 34/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 111.9867 - mae: 111.9867 - val_loss: 604.7632 - val_mae: 604.7632\n",
            "Epoch 35/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.9451 - mae: 110.9451 - val_loss: 593.4648 - val_mae: 593.4648\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 36/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 114.4816 - mae: 114.4816 - val_loss: 608.0073 - val_mae: 608.0073\n",
            "Epoch 37/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.2017 - mae: 110.2017 - val_loss: 597.2309 - val_mae: 597.2309\n",
            "Epoch 38/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 112.2372 - mae: 112.2372 - val_loss: 637.9797 - val_mae: 637.9797\n",
            "Epoch 39/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 115.1289 - mae: 115.1289 - val_loss: 587.4679 - val_mae: 587.4679\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 40/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 110.0854 - mae: 110.0854 - val_loss: 592.7117 - val_mae: 592.7117\n",
            "Epoch 41/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.6343 - mae: 110.6343 - val_loss: 593.8997 - val_mae: 593.8997\n",
            "Epoch 42/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 113.5762 - mae: 113.5762 - val_loss: 636.3674 - val_mae: 636.3674\n",
            "Epoch 43/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 116.2286 - mae: 116.2286 - val_loss: 662.9264 - val_mae: 662.9264\n",
            "Epoch 44/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 120.0192 - mae: 120.0192 - val_loss: 635.6360 - val_mae: 635.6360\n",
            "Epoch 45/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.9675 - mae: 110.9675 - val_loss: 601.9926 - val_mae: 601.9926\n",
            "Epoch 46/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 111.6012 - mae: 111.6012 - val_loss: 593.3531 - val_mae: 593.3531\n",
            "Epoch 47/100\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 109.6161 - mae: 109.6161 - val_loss: 637.0014 - val_mae: 637.0014\n",
            "Epoch 48/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 109.1368 - mae: 109.1368 - val_loss: 598.4199 - val_mae: 598.4199\n",
            "Epoch 49/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 112.4355 - mae: 112.4355 - val_loss: 579.7040 - val_mae: 579.7040\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 50/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.2108 - mae: 110.2108 - val_loss: 639.2326 - val_mae: 639.2326\n",
            "Epoch 51/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 111.0958 - mae: 111.0958 - val_loss: 597.3575 - val_mae: 597.3575\n",
            "Epoch 52/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.7351 - mae: 110.7351 - val_loss: 580.7227 - val_mae: 580.7227\n",
            "Epoch 53/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 111.1785 - mae: 111.1785 - val_loss: 648.3588 - val_mae: 648.3588\n",
            "Epoch 54/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 114.0832 - mae: 114.0832 - val_loss: 593.2007 - val_mae: 593.2007\n",
            "Epoch 55/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.4910 - mae: 110.4910 - val_loss: 579.5065 - val_mae: 579.5065\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 56/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 108.0489 - mae: 108.0489 - val_loss: 807.3851 - val_mae: 807.3851\n",
            "Epoch 57/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 125.0614 - mae: 125.0614 - val_loss: 674.1654 - val_mae: 674.1654\n",
            "Epoch 58/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 115.4340 - mae: 115.4340 - val_loss: 582.2698 - val_mae: 582.2698\n",
            "Epoch 59/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 110.0881 - mae: 110.0881 - val_loss: 606.7637 - val_mae: 606.7637\n",
            "Epoch 60/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.7156 - mae: 108.7156 - val_loss: 602.3102 - val_mae: 602.3102\n",
            "Epoch 61/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.1525 - mae: 108.1525 - val_loss: 573.9990 - val_mae: 573.9990\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 62/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 107.3727 - mae: 107.3727 - val_loss: 581.7012 - val_mae: 581.7012\n",
            "Epoch 63/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.7667 - mae: 110.7667 - val_loss: 637.5252 - val_mae: 637.5252\n",
            "Epoch 64/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 110.1539 - mae: 110.1539 - val_loss: 586.6601 - val_mae: 586.6601\n",
            "Epoch 65/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 108.2325 - mae: 108.2325 - val_loss: 573.5620 - val_mae: 573.5620\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 66/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 108.6825 - mae: 108.6825 - val_loss: 572.2206 - val_mae: 572.2206\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 67/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 106.6371 - mae: 106.6371 - val_loss: 646.6349 - val_mae: 646.6349\n",
            "Epoch 68/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 114.1603 - mae: 114.1603 - val_loss: 681.8561 - val_mae: 681.8561\n",
            "Epoch 69/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 124.5514 - mae: 124.5514 - val_loss: 655.9885 - val_mae: 655.9885\n",
            "Epoch 70/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 125.0235 - mae: 125.0235 - val_loss: 601.0032 - val_mae: 601.0032\n",
            "Epoch 71/100\n",
            "18/18 [==============================] - 0s 6ms/step - loss: 110.3652 - mae: 110.3652 - val_loss: 595.3962 - val_mae: 595.3962\n",
            "Epoch 72/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 107.9285 - mae: 107.9285 - val_loss: 573.7085 - val_mae: 573.7085\n",
            "Epoch 73/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 109.5085 - mae: 109.5085 - val_loss: 580.4180 - val_mae: 580.4180\n",
            "Epoch 74/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.7380 - mae: 108.7380 - val_loss: 576.1211 - val_mae: 576.1211\n",
            "Epoch 75/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 107.9404 - mae: 107.9404 - val_loss: 591.1477 - val_mae: 591.1477\n",
            "Epoch 76/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 109.4232 - mae: 109.4232 - val_loss: 597.8605 - val_mae: 597.8605\n",
            "Epoch 77/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 107.5879 - mae: 107.5879 - val_loss: 571.9299 - val_mae: 571.9299\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 78/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.1598 - mae: 108.1598 - val_loss: 575.2383 - val_mae: 575.2383\n",
            "Epoch 79/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 107.9175 - mae: 107.9175 - val_loss: 617.3071 - val_mae: 617.3071\n",
            "Epoch 80/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.9510 - mae: 108.9510 - val_loss: 583.4847 - val_mae: 583.4847\n",
            "Epoch 81/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 106.0505 - mae: 106.0505 - val_loss: 570.0802 - val_mae: 570.0802\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 82/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 115.6827 - mae: 115.6827 - val_loss: 575.7382 - val_mae: 575.7382\n",
            "Epoch 83/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 110.9379 - mae: 110.9379 - val_loss: 659.6570 - val_mae: 659.6570\n",
            "Epoch 84/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 111.4836 - mae: 111.4836 - val_loss: 570.1959 - val_mae: 570.1959\n",
            "Epoch 85/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 107.5948 - mae: 107.5948 - val_loss: 601.5945 - val_mae: 601.5945\n",
            "Epoch 86/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.9426 - mae: 108.9426 - val_loss: 592.8107 - val_mae: 592.8107\n",
            "Epoch 87/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 105.7717 - mae: 105.7717 - val_loss: 603.6169 - val_mae: 603.6169\n",
            "Epoch 88/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 107.9217 - mae: 107.9217 - val_loss: 569.0500 - val_mae: 569.0500\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 89/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 106.0344 - mae: 106.0344 - val_loss: 568.9512 - val_mae: 568.9512\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_1_dense/assets\n",
            "Epoch 90/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 105.4977 - mae: 105.4977 - val_loss: 581.7681 - val_mae: 581.7681\n",
            "Epoch 91/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 108.8468 - mae: 108.8468 - val_loss: 573.6023 - val_mae: 573.6023\n",
            "Epoch 92/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 110.8884 - mae: 110.8884 - val_loss: 576.8247 - val_mae: 576.8247\n",
            "Epoch 93/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 113.8781 - mae: 113.8781 - val_loss: 608.3018 - val_mae: 608.3018\n",
            "Epoch 94/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 110.5763 - mae: 110.5763 - val_loss: 601.6047 - val_mae: 601.6047\n",
            "Epoch 95/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 106.5906 - mae: 106.5906 - val_loss: 570.3652 - val_mae: 570.3652\n",
            "Epoch 96/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 116.9515 - mae: 116.9515 - val_loss: 615.2581 - val_mae: 615.2581\n",
            "Epoch 97/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 108.0739 - mae: 108.0739 - val_loss: 580.3073 - val_mae: 580.3073\n",
            "Epoch 98/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 108.7102 - mae: 108.7102 - val_loss: 586.6512 - val_mae: 586.6512\n",
            "Epoch 99/100\n",
            "18/18 [==============================] - 0s 5ms/step - loss: 109.0488 - mae: 109.0488 - val_loss: 570.0629 - val_mae: 570.0629\n",
            "Epoch 100/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 106.1845 - mae: 106.1845 - val_loss: 585.9763 - val_mae: 585.9763\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdcf48bd110>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRrvDINUooJO"
      },
      "source": [
        "Because of the small size of our data (less than 3000 total samples), the model trains very fast.\n",
        "\n",
        "Let's evaluate it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1DbcEZBY_Bt",
        "outputId": "2d353c8c-075c-4149-bd68-9fb6b25a44d4"
      },
      "source": [
        "# Evaluate model on test data\n",
        "model_1.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 585.9762 - mae: 585.9762\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[585.9761962890625, 585.9761962890625]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwaUv85oox2F"
      },
      "source": [
        "You'll notice the model achieves the same `val_loss` (in this case, this is MAE) as the last epoch.\n",
        "\n",
        "But if we load in the version of `model_1` which was saved to file using the `ModelCheckpoint` callback, we should see an improvement in results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXey-AX-XZPK",
        "outputId": "98d82f0b-ce9e-4326-f456-f1e49f5e8483"
      },
      "source": [
        "# Load in saved best performing model_1 and evaluate on test data\n",
        "model_1 = tf.keras.models.load_model(\"model_experiments/model_1_dense\")\n",
        "model_1.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 3ms/step - loss: 568.9512 - mae: 568.9512\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[568.951171875, 568.951171875]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvVtmLRdpcyH"
      },
      "source": [
        "Much better! Due to the fluctuating performance of the model during training, loading back in the best performing model see's a sizeable improvement in MAE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HfeTX5KhYin"
      },
      "source": [
        "## Making forecasts with a model (on the test dataset)\n",
        "\n",
        "We've trained a model and evaluated the it on the test data, but the project we're working on is called BitPredict 💰📈 so how do you think we could use our model to make predictions?\n",
        "\n",
        "Since we're going to be running more modelling experiments, let's write a function which: \n",
        "1. Takes in a trained model (just like `model_1`)\n",
        "2. Takes in some input data (just like the data the model was trained on) \n",
        "3. Passes the input data to the model's `predict()` method\n",
        "4. Returns the predictions\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZrExWzyh3ur"
      },
      "source": [
        "def make_preds(model, input_data):\n",
        "  \"\"\"\n",
        "  Uses model to make predictions on input_data.\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  model: trained model \n",
        "  input_data: windowed input data (same kind of data model was trained on)\n",
        "\n",
        "  Returns model predictions on input_data.\n",
        "  \"\"\"\n",
        "  forecast = model.predict(input_data)\n",
        "  return tf.squeeze(forecast) # return 1D array of predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcsoVIrGvrDd"
      },
      "source": [
        "Nice! \n",
        "\n",
        "Now let's use our `make_preds()` and see how it goes.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1aAKQp6vapw",
        "outputId": "0e3faaae-e3fb-4196-9139-c6c5f5efb9a1"
      },
      "source": [
        "# Make predictions using model_1 on the test dataset and view the results\n",
        "model_1_preds = make_preds(model_1, test_windows)\n",
        "len(model_1_preds), model_1_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(556, <tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              " array([8861.711, 8769.886, 9015.71 , 8795.517, 8723.809, 8730.11 ,\n",
              "        8691.95 , 8502.054, 8460.961, 8516.547], dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5Ra13OrwIOf"
      },
      "source": [
        "> 🔑 **Note:** With these outputs, our model isn't *forecasting* yet. It's only making predictions on the test dataset. Forecasting would involve a model making predictions into the future, however, the test dataset is only a pseudofuture.\n",
        "\n",
        "Excellent! Now we've got some prediction values, let's use the `evaluate_preds()` we created before to compare them to the ground truth."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eHak7Jqd9aXv",
        "outputId": "6969b6f5-1922-46c4-934d-48a24c452807"
      },
      "source": [
        "# Evaluate preds\n",
        "model_1_results = evaluate_preds(y_true=tf.squeeze(test_labels), # reduce to right shape\n",
        "                                 y_pred=model_1_preds)\n",
        "model_1_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 568.95123,\n",
              " 'mape': 2.5448983,\n",
              " 'mase': 0.9994897,\n",
              " 'mse': 1171744.0,\n",
              " 'rmse': 1082.4713}"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGL9Mj6Rwddw"
      },
      "source": [
        "How did our model go? Did it beat the naïve forecast?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WePaRbqxTXL",
        "outputId": "c80b91ed-3c80-42aa-ade9-48cd89e98dd9"
      },
      "source": [
        "naive_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 567.9802,\n",
              " 'mape': 2.516525,\n",
              " 'mase': 0.99957,\n",
              " 'mse': 1147547.0,\n",
              " 'rmse': 1071.2362}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skEwK88PxhDL"
      },
      "source": [
        "It looks like our naïve model beats our first deep model on nearly every metric.\n",
        "\n",
        "That goes to show the power of the naïve model and the reason for having a baseline for any machine learning project.\n",
        "\n",
        "And of course, no evaluation would be finished without visualizing the results.\n",
        "\n",
        "Let's use the `plot_time_series()` function to plot `model_1_preds` against the test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "Wgxoqg_tIhFJ",
        "outputId": "018c00f6-e663-49d6-d046-177e200ccbfb"
      },
      "source": [
        "offset = 300\n",
        "plt.figure(figsize=(10, 7))\n",
        "# Account for the test_window offset and index into test_labels to ensure correct plotting\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], values=test_labels[:, 0], start=offset, label=\"Test_data\")\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], values=model_1_preds, start=offset, format=\"-\", label=\"model_1_preds\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiZLQ4KpqRqN"
      },
      "source": [
        "What's wrong with these predictions?\n",
        "\n",
        "As mentioned before, they're on the test dataset. So they're not actual forecasts.\n",
        "\n",
        "With our current model setup, how do you think we'd make forecasts for the future?\n",
        "\n",
        "Have a think about it for now, we'll cover this later on."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fr18ffVq4i6m"
      },
      "source": [
        "## Model 2: Dense (window = 30, horizon = 1)\n",
        "\n",
        "A naïve model is currently beating our handcrafted deep learning model. \n",
        "\n",
        "We can't let this happen.\n",
        "\n",
        "Let's continue our modelling experiments.\n",
        "\n",
        "We'll keep the previous model architecture but use a window size of 30.\n",
        "\n",
        "In other words, we'll use the previous 30 days of Bitcoin prices to try and predict the next day price.\n",
        "\n",
        "![bitcoin prices windowed for 30 days to predict a horizon of 1 day](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-bitcoin-price-window-for-one-month.png)\n",
        "*Example of Bitcoin prices windowed for 30 days to predict a horizon of 1.*\n",
        "\n",
        "> 🔑 **Note:** Recall from before, the window size (how many timesteps to use to fuel a forecast) and the horizon (how many timesteps to predict into the future) are **hyperparameters**. This means you can tune them to try and find values will result in better performance.\n",
        "\n",
        "We'll start our second modelling experiment by preparing datasets using the functions we created earlier."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vz0eMi7F4wpn"
      },
      "source": [
        "HORIZON = 1 # predict one step at a time\n",
        "WINDOW_SIZE = 30 # use 30 timesteps in the past"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HlYaAxr6UoC",
        "outputId": "7fdc8064-6fe8-4739-919d-dc0c06de8666"
      },
      "source": [
        "# Make windowed data with appropriate horizon and window sizes\n",
        "full_windows, full_labels = make_windows(prices, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(full_windows), len(full_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2757, 2757)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWxYZtOuelzO",
        "outputId": "14a862eb-c90e-4905-fbae-640d890059b7"
      },
      "source": [
        "# Make train and testing windows\n",
        "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(windows=full_windows, labels=full_labels)\n",
        "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2205, 552, 2205, 552)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xikvf8N0_dU"
      },
      "source": [
        "Data prepared!\n",
        "\n",
        "Now let's construct `model_2`, a model with the same architecture as `model_1` as well as the same training routine."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_oz5aJE6UwN",
        "outputId": "e9f156de-6b38-44b4-e319-0d5d1be0ffa4"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create model (same model as model 1 but data input will be different)\n",
        "model_2 = tf.keras.Sequential([\n",
        "  layers.Dense(128, activation=\"relu\"),\n",
        "  layers.Dense(HORIZON) # need to predict horizon number of steps into the future\n",
        "], name=\"model_2_dense\")\n",
        "\n",
        "model_2.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "\n",
        "model_2.fit(train_windows,\n",
        "            train_labels,\n",
        "            epochs=100,\n",
        "            batch_size=128,\n",
        "            verbose=0,\n",
        "            validation_data=(test_windows, test_labels),\n",
        "            callbacks=[create_model_checkpoint(model_name=model_2.name)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_2_dense/assets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdcf1094290>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0oYd2nt51Ppj"
      },
      "source": [
        "Once again, training goes nice and fast.\n",
        "\n",
        "Let's evaluate our model's performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W42Fa-aO8rhg",
        "outputId": "06a8f364-fc40-4216-aa7f-913c2689e589"
      },
      "source": [
        "# Evaluate model 2 preds\n",
        "model_2.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 608.9615\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608.9614868164062"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1P2DRrA1VlP"
      },
      "source": [
        "Hmmm... is that the best it did?\n",
        "\n",
        "How about we try loading in the best performing `model_2` which was saved to file thanks to our `ModelCheckpoint` callback."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_hG89F5YV4i",
        "outputId": "027e0c90-f19f-4478-f633-1a541ca73e39"
      },
      "source": [
        "# Load in best performing model\n",
        "model_2 = tf.keras.models.load_model(\"model_experiments/model_2_dense/\")\n",
        "model_2.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 608.9615\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608.9614868164062"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGh8gRhU1kaj"
      },
      "source": [
        "Excellent! Loading back in the best performing model see's a performance boost.\n",
        "\n",
        "But let's not stop there, let's make some predictions with `model_2` and then evaluate them just as we did before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPfvPgkUknY5"
      },
      "source": [
        "# Get forecast predictions\n",
        "model_2_preds = make_preds(model_2,\n",
        "                           input_data=test_windows)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dy7XZx7FllHX",
        "outputId": "f08daef8-841a-4c58-c8a6-da36b98ff47a"
      },
      "source": [
        "# Evaluate results for model 2 predictions\n",
        "model_2_results = evaluate_preds(y_true=tf.squeeze(test_labels), # remove 1 dimension of test labels\n",
        "                                 y_pred=model_2_preds)\n",
        "model_2_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 608.9615,\n",
              " 'mape': 2.7693386,\n",
              " 'mase': 1.0644706,\n",
              " 'mse': 1281438.8,\n",
              " 'rmse': 1132.0065}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8djCXli1wGy"
      },
      "source": [
        "It looks like `model_2` performs worse than the naïve model as well as `model_1`!\n",
        "\n",
        "Does this mean a smaller window size is better? (I'll leave this as a challenge you can experiment with)\n",
        "\n",
        "How do the predictions look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "jHh_qtXflx7Z",
        "outputId": "b5329370-f1c8-4980-b612-ed904880c16f"
      },
      "source": [
        "offset = 300\n",
        "plt.figure(figsize=(10, 7))\n",
        "# Account for the test_window offset\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], values=test_labels[:, 0], start=offset, label=\"test_data\")\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], values=model_2_preds, start=offset, format=\"-\", label=\"model_2_preds\") "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sls7mUaq9lMl"
      },
      "source": [
        "## Model 3: Dense (window = 30, horizon = 7)\n",
        "\n",
        "Let's try and predict 7 days ahead given the previous 30 days.\n",
        "\n",
        "First, we'll update the `HORIZON` and `WINDOW_SIZE` variables and create windowed data.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8PtmayTM98Mb",
        "outputId": "3bbaec11-0558-4181-d906-258ece66a3a3"
      },
      "source": [
        "HORIZON = 7\n",
        "WINDOW_SIZE = 30\n",
        "\n",
        "full_windows, full_labels = make_windows(prices, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(full_windows), len(full_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2751, 2751)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7lhMfxWZK83"
      },
      "source": [
        "And we'll split the full dataset windows into training and test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E8xgoKunZ__e",
        "outputId": "f6588b6d-2d90-44db-8062-ca1ffe03bfc8"
      },
      "source": [
        "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(windows=full_windows, labels=full_labels, test_split=0.2)\n",
        "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2200, 551, 2200, 551)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dlwu4mQmZvSg"
      },
      "source": [
        "Now let's build, compile, fit and evaluate a model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxHh7xS2-CoH",
        "outputId": "587403ef-8c0d-4c6d-928a-a6dddecc1794"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create model (same as model_1 except with different data input size)\n",
        "model_3 = tf.keras.Sequential([\n",
        "  layers.Dense(128, activation=\"relu\"),\n",
        "  layers.Dense(HORIZON)\n",
        "], name=\"model_3_dense\")\n",
        "\n",
        "model_3.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "\n",
        "model_3.fit(train_windows,\n",
        "            train_labels,\n",
        "            batch_size=128,\n",
        "            epochs=100,\n",
        "            verbose=0,\n",
        "            validation_data=(test_windows, test_labels),\n",
        "            callbacks=[create_model_checkpoint(model_name=model_3.name)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_3_dense/assets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdcf1d36350>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pd-8pbfh-DDI",
        "outputId": "ad359823-115b-4f42-f5da-bc5c2116951f"
      },
      "source": [
        "# How did our model with a larger window size and horizon go?\n",
        "model_3.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 1321.5201\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1321.5201416015625"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXqmKsQhS2qB"
      },
      "source": [
        "To compare apples to apples (best performing model to best performing model), we've got to load in the best version of `model_3`. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XsltcXHYSrkX",
        "outputId": "ca492549-6b42-4f67-c84b-37a77b742732"
      },
      "source": [
        "# Load in best version of model_3 and evaluate\n",
        "model_3 = tf.keras.models.load_model(\"model_experiments/model_3_dense/\")\n",
        "model_3.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 1237.5063\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1237.50634765625"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q--5dtgITJbc"
      },
      "source": [
        "In this case, the error will be higher because we're predicting 7 steps at a time. \n",
        "\n",
        "This makes sense though because the further you try and predict, the larger your error will be (think of trying to predict the weather 7 days in advance).\n",
        "\n",
        "Let's make predictions with our model using the `make_preds()` function and evaluate them using the `evaluate_preds()` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKBNVovRpvhd",
        "outputId": "4adc4cf3-362d-468b-9664-f63c8bbfc493"
      },
      "source": [
        "# The predictions are going to be 7 steps at a time (this is the HORIZON size)\n",
        "model_3_preds = make_preds(model_3,\n",
        "                           input_data=test_windows)\n",
        "model_3_preds[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 7), dtype=float32, numpy=\n",
              "array([[9004.693 , 9048.1   , 9425.088 , 9258.258 , 9495.798 , 9558.451 ,\n",
              "        9357.354 ],\n",
              "       [8735.507 , 8840.304 , 9247.793 , 8885.6   , 9097.188 , 9174.328 ,\n",
              "        9156.819 ],\n",
              "       [8672.508 , 8782.388 , 9123.8545, 8770.37  , 9007.13  , 9003.87  ,\n",
              "        9042.724 ],\n",
              "       [8874.398 , 8784.737 , 9043.901 , 8943.051 , 9033.479 , 9176.488 ,\n",
              "        9039.676 ],\n",
              "       [8825.891 , 8777.4375, 8926.779 , 8870.178 , 9213.232 , 9268.156 ,\n",
              "        8942.485 ]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9VQqenW-5to",
        "outputId": "d31e64e1-3827-4e87-e88c-24404164890c"
      },
      "source": [
        "# Calculate model_3 results - these are going to be multi-dimensional because\n",
        "# we're trying to predict more than one step at a time.\n",
        "model_3_results = evaluate_preds(y_true=tf.squeeze(test_labels),\n",
        "                                 y_pred=model_3_preds)\n",
        "model_3_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        17.28017   ], dtype=float32),\n",
              " 'mase': 2.2020736,\n",
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              "        1.74778820e+07, 2.13824600e+07, 5.77506950e+06, 7.30700800e+06,\n",
              "        3.70581275e+06, 6.14524500e+05, 1.92765088e+06, 2.29573075e+06,\n",
              "        1.09031975e+06, 1.19423488e+06, 2.01535425e+06, 1.38583700e+06,\n",
              "        2.60915325e+06, 7.48127550e+06, 1.25728690e+07, 1.49188240e+07,\n",
              "        7.64712100e+06, 9.50861100e+06, 1.16537410e+07, 2.15388360e+07,\n",
              "        4.32744960e+07, 6.52254160e+07, 9.30125040e+07, 8.49970400e+07,\n",
              "        5.87007080e+07, 3.43532560e+07, 2.82685780e+07, 9.30442400e+06,\n",
              "        4.66932300e+06, 8.00189950e+06, 2.05975580e+07, 4.07234120e+07,\n",
              "        1.93118840e+07, 4.04353375e+06, 5.92508700e+06, 7.37346950e+06,\n",
              "        2.68488000e+06, 5.27761500e+06, 1.84823362e+06, 4.87232050e+06,\n",
              "        4.87888400e+06, 5.77534400e+06, 1.21702240e+07, 2.70199400e+07,\n",
              "        4.94704840e+07, 6.90300960e+07, 8.50554880e+07], dtype=float32),\n",
              " 'rmse': array([  561.7492  ,   411.2974  ,   379.64694 ,   419.52707 ,\n",
              "          513.34174 ,   625.7134  ,   632.07965 ,   586.87506 ,\n",
              "          702.7746  ,   829.9311  ,   958.4903  ,   894.97943 ,\n",
              "          797.1879  ,   803.452   ,   598.1137  ,   320.94724 ,\n",
              "          230.89664 ,   215.8309  ,   332.18848 ,   334.13724 ,\n",
              "          208.65266 ,   233.53539 ,   213.50632 ,   276.5658  ,\n",
              "          200.76715 ,   164.6427  ,   137.93799 ,   213.45375 ,\n",
              "          206.23912 ,   257.53568 ,   341.67487 ,   411.6321  ,\n",
              "          332.3958  ,   399.6341  ,   402.03375 ,   351.9401  ,\n",
              "          388.27478 ,   362.2103  ,   234.58073 ,   250.63026 ,\n",
              "          421.2771  ,   163.96461 ,   159.07036 ,   160.63757 ,\n",
              "          116.014   ,    62.305946,    96.06105 ,   110.63433 ,\n",
              "          148.61641 ,   189.95403 ,   220.89862 ,   193.55951 ,\n",
              "          202.62302 ,   204.30829 ,   332.4016  ,   448.06476 ,\n",
              "          590.04156 ,   598.38184 ,   573.0404  ,   544.5235  ,\n",
              "          483.0674  ,   392.4885  ,   378.37988 ,   403.22012 ,\n",
              "          544.47473 ,   516.605   ,   542.39197 ,   556.8372  ,\n",
              "          250.43535 ,   293.91644 ,   311.3752  ,   449.23752 ,\n",
              "          545.02625 ,   447.9675  ,   356.79846 ,   364.01865 ,\n",
              "          290.8643  ,   418.11176 ,   543.24567 ,   689.84686 ,\n",
              "          849.64374 ,   680.2288  ,   364.9038  ,   285.2438  ,\n",
              "          185.23193 ,   185.7967  ,   231.7006  ,   272.47263 ,\n",
              "          330.66504 ,   528.85046 ,   468.10193 ,   447.1402  ,\n",
              "          407.3124  ,   285.7863  ,   219.63496 ,   406.57227 ,\n",
              "          426.26212 ,   575.0174  ,   680.9007  ,   821.6106  ,\n",
              "          578.7154  ,   411.51398 ,   321.27753 ,   385.1592  ,\n",
              "          546.5207  ,   609.8793  ,   665.8128  ,   726.57056 ,\n",
              "         1062.7572  ,  1003.25934 ,   896.2404  ,   470.40195 ,\n",
              "          257.8539  ,   163.15088 ,   181.57336 ,   410.6863  ,\n",
              "          419.83    ,   547.14307 ,   657.1099  ,  1383.2812  ,\n",
              "         1975.7374  ,  2508.9888  ,  2343.1653  ,  2501.3535  ,\n",
              "         2507.2256  ,  2690.495   ,  1442.718   ,   816.56995 ,\n",
              "          380.3155  ,   428.26813 ,   583.9748  ,   549.6087  ,\n",
              "          609.3766  ,   449.90912 ,   368.98663 ,   287.18607 ,\n",
              "          442.72644 ,   401.98962 ,   414.00653 ,   447.891   ,\n",
              "          487.03067 ,   398.39697 ,   252.1964  ,   582.15466 ,\n",
              "          723.54974 ,   534.98804 ,   438.55002 ,   193.33018 ,\n",
              "          363.26575 ,   305.47717 ,   344.15646 ,   548.26886 ,\n",
              "          478.53625 ,   598.29285 ,   486.0237  ,   414.16226 ,\n",
              "          163.85437 ,   218.89795 ,   200.1048  ,   232.60092 ,\n",
              "          328.0875  ,   313.0527  ,   266.14847 ,   256.1221  ,\n",
              "          292.1883  ,   365.8382  ,   522.03094 ,   808.7852  ,\n",
              "          705.1331  ,   781.7865  ,   996.4475  ,   978.1065  ,\n",
              "         1046.6204  ,  1092.1682  ,   612.37427 ,   471.22586 ,\n",
              "          510.50952 ,   433.7161  ,   433.50607 ,   499.05774 ,\n",
              "          615.78625 ,   638.5192  ,   790.4321  ,   980.85645 ,\n",
              "          891.8209  ,   374.91006 ,   515.0024  ,   791.3719  ,\n",
              "          453.65652 ,   255.55615 ,   365.4627  ,   338.66217 ,\n",
              "          476.35388 ,   511.10318 ,   713.1032  ,   700.5317  ,\n",
              "          488.5917  ,   261.8312  ,   270.2918  ,   231.54132 ,\n",
              "          572.908   ,   791.48425 ,   719.5384  ,   576.6594  ,\n",
              "          415.52045 ,   287.7739  ,   378.6722  ,   297.18173 ,\n",
              "          227.1226  ,   205.13364 ,   421.95868 ,   342.56616 ,\n",
              "          341.86316 ,   320.02628 ,   409.25394 ,   453.33206 ,\n",
              "          522.70593 ,   217.68823 ,   214.61069 ,   231.02502 ,\n",
              "          280.54785 ,   265.7982  ,   228.72073 ,   158.20264 ,\n",
              "          212.60869 ,   203.64981 ,   264.5954  ,   352.3239  ,\n",
              "          366.2897  ,   581.72296 ,   312.34595 ,   331.69714 ,\n",
              "          141.1364  ,   182.4111  ,   156.11276 ,   166.31807 ,\n",
              "          164.43439 ,   125.7441  ,   107.780495,   165.78665 ,\n",
              "          171.83319 ,   198.96605 ,   167.8827  ,   178.64919 ,\n",
              "          236.72102 ,   199.58418 ,   224.09854 ,   178.8182  ,\n",
              "          172.08786 ,   211.73892 ,   207.91    ,   123.46075 ,\n",
              "          169.1382  ,   205.78828 ,   269.2643  ,   349.1772  ,\n",
              "          800.93115 ,   922.2048  ,  1054.2076  ,  1104.5502  ,\n",
              "         1241.4113  ,  1426.3665  ,  1328.3093  ,   649.6928  ,\n",
              "          361.26694 ,   270.29266 ,   292.59366 ,   272.4704  ,\n",
              "          386.6093  ,   112.081894,   145.97955 ,   313.3362  ,\n",
              "          269.8181  ,   355.05597 ,   318.33624 ,   352.9209  ,\n",
              "          267.0955  ,   290.16382 ,   250.05786 ,   286.145   ,\n",
              "          356.79572 ,   299.1089  ,   322.64612 ,   407.6114  ,\n",
              "          481.7431  ,   664.8045  ,   419.33078 ,   420.05396 ,\n",
              "          302.0214  ,   409.84808 ,   433.44717 ,   328.0807  ,\n",
              "          180.71472 ,   231.3898  ,   371.67667 ,   573.2698  ,\n",
              "          799.3082  ,  1040.5736  ,  1248.2089  ,  1556.075   ,\n",
              "         1445.6144  ,   991.4733  ,   653.7855  ,   241.664   ,\n",
              "          200.71255 ,   229.69164 ,   365.6423  ,   491.61737 ,\n",
              "          504.54974 ,   467.4137  ,   489.7394  ,   415.83694 ,\n",
              "          307.593   ,   285.44888 ,   428.70956 ,   584.49255 ,\n",
              "          516.80835 ,   595.6676  ,   411.8345  ,   162.18913 ,\n",
              "          145.37505 ,   334.1946  ,   246.74936 ,   215.40991 ,\n",
              "          264.1329  ,   341.95923 ,   380.3039  ,   338.56595 ,\n",
              "          281.5662  ,   238.5704  ,   174.35591 ,   307.85596 ,\n",
              "          345.94073 ,   464.11203 ,   561.7188  ,   646.22894 ,\n",
              "          625.9026  ,   532.09955 ,   285.625   ,   223.7945  ,\n",
              "          318.74115 ,   248.5351  ,   562.78455 ,   759.4393  ,\n",
              "          860.8736  ,  1154.201   ,  1251.0643  ,  1247.2102  ,\n",
              "         1298.1444  ,   567.3183  ,   251.13608 ,   278.9937  ,\n",
              "          226.39412 ,   338.29608 ,   336.92746 ,   265.14423 ,\n",
              "          217.05034 ,   672.6335  ,   882.671   ,   855.88336 ,\n",
              "         1019.35803 ,  1249.835   ,  1280.4559  ,  1314.4961  ,\n",
              "          632.8708  ,   484.36414 ,   420.38657 ,   260.38715 ,\n",
              "          624.8056  ,   990.02106 ,  1017.5319  ,   919.0592  ,\n",
              "         1178.533   ,  1408.3364  ,  1904.8239  ,  1633.771   ,\n",
              "         1192.1284  ,   674.242   ,   604.9521  ,   972.9281  ,\n",
              "         1204.9263  ,  1363.4519  ,  1378.1527  ,  1350.4559  ,\n",
              "         1101.2997  ,   703.3076  ,  1223.3164  ,  1425.1029  ,\n",
              "         1502.1896  ,   459.54623 ,   348.41965 ,   551.64197 ,\n",
              "          843.7437  ,   787.5989  ,   897.4333  ,   791.77203 ,\n",
              "          752.5888  ,   653.84814 ,   885.1505  ,  1905.3235  ,\n",
              "         2860.1357  ,  3281.406   ,  3557.5776  ,  3653.7703  ,\n",
              "         3660.019   ,  2554.4297  ,  1267.6624  ,   925.6123  ,\n",
              "         1075.2522  ,  1301.0342  ,  1473.5092  ,  1828.1636  ,\n",
              "         2801.7761  ,  3292.0957  ,  3251.5688  ,  3187.5154  ,\n",
              "         3379.4363  ,  3408.5151  ,  4165.453   ,  4234.4043  ,\n",
              "         5202.1772  ,  6104.635   ,  5456.073   ,  4632.28    ,\n",
              "         4940.305   ,  3949.767   ,  3221.3562  ,  3152.0356  ,\n",
              "         4678.5625  ,  5873.6855  ,  5220.106   ,  1586.0177  ,\n",
              "         1453.9965  ,  1284.9175  ,  2963.2163  ,  3771.7898  ,\n",
              "         3876.5652  ,  3776.622   ,  3339.4758  ,  3624.7173  ,\n",
              "         3798.29    ,  1451.92    ,  1540.3851  ,  1437.0847  ,\n",
              "         1229.9243  ,  1279.0306  ,  2451.6096  ,  3849.44    ,\n",
              "         3891.8213  ,  2981.3203  ,  3219.5703  ,  3524.72    ,\n",
              "         5588.383   ,  6664.252   ,  5793.246   ,  5805.394   ,\n",
              "         6580.4614  ,  6361.1904  ,  8173.865   ,  5178.2466  ,\n",
              "         1836.379   ,  2486.362   ,  1826.4835  ,  3124.1047  ,\n",
              "         3083.0798  ,  3850.9783  ,  3287.689   ,  3909.1228  ,\n",
              "         3482.797   ,  3835.5054  ,  6679.7974  ,  7869.091   ,\n",
              "        10282.95    ,  8978.149   ,  5480.48    ,  3616.3398  ,\n",
              "         2135.3894  ,  1295.878   ,  2644.6782  ,  4043.3542  ,\n",
              "         3485.6704  ,  3489.9646  ,  4110.7144  ,  4892.719   ,\n",
              "         6107.91    ,  7138.7007  ,  6778.814   ,  5656.8877  ,\n",
              "         4542.8135  ,  2853.957   ,  2036.6534  ,  2057.9094  ,\n",
              "         1760.0701  ,  2894.6804  ,  2713.1316  ,  2657.2437  ,\n",
              "         3966.2427  ,  5279.841   ,  6382.3184  ,  5731.68    ,\n",
              "         5376.0254  ,  1844.5288  ,  1960.1613  ,  2847.4656  ,\n",
              "         4180.656   ,  4624.117   ,  2403.1375  ,  2703.1477  ,\n",
              "         1925.0488  ,   783.9162  ,  1388.3987  ,  1515.1669  ,\n",
              "         1044.1838  ,  1092.8105  ,  1419.6317  ,  1177.2158  ,\n",
              "         1615.2874  ,  2735.192   ,  3545.8242  ,  3862.4895  ,\n",
              "         2765.3428  ,  3083.6038  ,  3413.7578  ,  4640.995   ,\n",
              "         6578.3354  ,  8076.225   ,  9644.3     ,  9219.384   ,\n",
              "         7661.638   ,  5861.165   ,  5316.8203  ,  3050.3152  ,\n",
              "         2160.8616  ,  2828.763   ,  4538.453   ,  6381.4897  ,\n",
              "         4394.529   ,  2010.854   ,  2434.1501  ,  2715.4133  ,\n",
              "         1638.5604  ,  2297.3062  ,  1359.4976  ,  2207.3335  ,\n",
              "         2208.8198  ,  2403.1946  ,  3488.5847  ,  5198.071   ,\n",
              "         7033.5254  ,  8308.436   ,  9222.554   ], dtype=float32)}"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t405o940Twum"
      },
      "source": [
        "## Make our evaluation function work for larger horizons\n",
        "\n",
        "You'll notice the outputs for `model_3_results` are multi-dimensional. \n",
        "\n",
        "This is because the predictions are getting evaluated across the `HORIZON` timesteps (7 predictions at a time).\n",
        "\n",
        "To fix this, let's adjust our `evaluate_preds()` function to work with multiple shapes of data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERkl26UFgSbz"
      },
      "source": [
        "def evaluate_preds(y_true, y_pred):\n",
        "  # Make sure float32 (for metric calculations)\n",
        "  y_true = tf.cast(y_true, dtype=tf.float32)\n",
        "  y_pred = tf.cast(y_pred, dtype=tf.float32)\n",
        "\n",
        "  # Calculate various metrics\n",
        "  mae = tf.keras.metrics.mean_absolute_error(y_true, y_pred)\n",
        "  mse = tf.keras.metrics.mean_squared_error(y_true, y_pred)\n",
        "  rmse = tf.sqrt(mse)\n",
        "  mape = tf.keras.metrics.mean_absolute_percentage_error(y_true, y_pred)\n",
        "  mase = mean_absolute_scaled_error(y_true, y_pred)\n",
        "\n",
        "  # Account for different sized metrics (for longer horizons, reduce to single number)\n",
        "  if mae.ndim > 0: # if mae isn't already a scalar, reduce it to one by aggregating tensors to mean\n",
        "    mae = tf.reduce_mean(mae)\n",
        "    mse = tf.reduce_mean(mse)\n",
        "    rmse = tf.reduce_mean(rmse)\n",
        "    mape = tf.reduce_mean(mape)\n",
        "    mase = tf.reduce_mean(mase)\n",
        "\n",
        "  return {\"mae\": mae.numpy(),\n",
        "          \"mse\": mse.numpy(),\n",
        "          \"rmse\": rmse.numpy(),\n",
        "          \"mape\": mape.numpy(),\n",
        "          \"mase\": mase.numpy()}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RnoZl28Udx4"
      },
      "source": [
        "Now we've updated `evaluate_preds()` to work with multiple shapes, how does it look?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kt1Z8yjpUij2",
        "outputId": "31690393-6057-40df-ef06-cfb3914d7ab4"
      },
      "source": [
        "# Get model_3 results aggregated to single values\n",
        "model_3_results = evaluate_preds(y_true=tf.squeeze(test_labels),\n",
        "                                 y_pred=model_3_preds)\n",
        "model_3_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 1237.5063,\n",
              " 'mape': 5.5588784,\n",
              " 'mase': 2.2020736,\n",
              " 'mse': 5405198.5,\n",
              " 'rmse': 1425.7477}"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XNIJfcsTpdA"
      },
      "source": [
        "Time to visualize.\n",
        "\n",
        "If our prediction evaluation metrics were mutli-dimensional, how do you think the predictions will look like if we plot them?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "gBdg1NcLs3gW",
        "outputId": "f9c0c6dd-39f5-42b2-9c5c-8020742b9318"
      },
      "source": [
        "offset = 300\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], values=test_labels[:, 0], start=offset, label=\"Test_data\")\n",
        "# Checking the shape of model_3_preds results in [n_test_samples, HORIZON] (this will screw up the plot)\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], values=model_3_preds, start=offset, label=\"model_3_preds\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3nZnSG1Uute"
      },
      "source": [
        "When we try to plot our multi-horizon predicts, we get a funky looking plot.\n",
        "\n",
        "Again, we can fix this by aggregating our model's predictions.\n",
        "\n",
        "> 🔑 **Note:** Aggregating the predictions (e.g. reducing a 7-day horizon to one value such as the mean) loses information from the original prediction. As in, the model predictions were trained to be made for 7-days but by reducing them to one, we gain the ability to plot them visually but we lose the extra information contained across multiple days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "wYpZb27bU6OO",
        "outputId": "c310346c-5cb9-4262-c52e-c468725aa4f0"
      },
      "source": [
        "offset = 300\n",
        "plt.figure(figsize=(10, 7))\n",
        "# Plot model_3_preds by aggregating them (note: this condenses information so the preds will look fruther ahead than the test data)\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], \n",
        "                 values=test_labels[:, 0], \n",
        "                 start=offset, \n",
        "                 label=\"Test_data\")\n",
        "plot_time_series(timesteps=X_test[-len(test_windows):], \n",
        "                 values=tf.reduce_mean(model_3_preds, axis=1), \n",
        "                 format=\"-\",\n",
        "                 start=offset, \n",
        "                 label=\"model_3_preds\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ojqon-Y5VpCC"
      },
      "source": [
        "## Which of our models is performing best so far?\n",
        "\n",
        "So far, we've trained 3 models which use the same architecture but use different data inputs.\n",
        "\n",
        "Let's compare them with the naïve model to see which model is performing the best so far."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "QA4EKseAVrYf",
        "outputId": "8c842c42-48b8-46ca-94f2-a6fc0344494f"
      },
      "source": [
        "pd.DataFrame({\"naive\": naive_results[\"mae\"], \n",
        "              \"horizon_1_window_7\": model_1_results[\"mae\"], \n",
        "              \"horizon_1_window_30\": model_2_results[\"mae\"], \n",
        "              \"horizon_7_window_30\": model_3_results[\"mae\"]}, index=[\"mae\"]).plot(figsize=(10, 7), kind=\"bar\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIRwia9BWeI5"
      },
      "source": [
        "Woah, our naïve model is performing best (it's very hard to beat a naïve model in open systems) but the dense model with a horizon of 1 and a window size of 7 looks to be performing cloest.\n",
        "\n",
        "Because of this, let's use `HORIZON=1` and `WINDOW_SIZE=7` for our next series of modelling experiments (in other words, we'll use the previous week of Bitcoin prices to try and predict the next day).\n",
        "\n",
        "> 🔑 **Note:** You might be wondering, why are the naïve results so good? One of the reasons could be due the presence of **autocorrelation** in the data. If a time series has **autocorrelation** it means the value at `t+1` (the next timestep) is typically close to the value at `t` (the current timestep). In other words, today's value is probably pretty close to yesterday's value. Of course, this isn't always the case but when it is, a naïve model will often get fairly good results.\n",
        "\n",
        "> 📖 **Resource:** For more on how autocorrelation influences a model's predictions, see the article [*How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls*](https://towardsdatascience.com/how-not-to-use-machine-learning-for-time-series-forecasting-avoiding-the-pitfalls-19f9d7adf424) by Vegard Flovik"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxcL0gi-rWC5"
      },
      "source": [
        "## Model 4: Conv1D\n",
        "\n",
        "Onto the next modelling experiment!\n",
        "\n",
        "This time, we'll be using a Conv1D model. Because as we saw in the sequence modelling notebook, Conv1D models can be used for seq2seq (sequence to sequence) problems.\n",
        "\n",
        "In our case, the input sequence is the previous 7 days of Bitcoin price data and the output is the next day (in seq2seq terms this is called a many to one problem).\n",
        "\n",
        "![bitcoin prediction in seq2seq terms](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-bitcoin-forecast-in-seq2seq-terms.png)\n",
        "*Framing Bitcoin forecasting in seq2seq (sequence to sequence) terms. Using a window size of 7 and a horizon of one results in a many to one problem. Using a window size of >1 and a horizon of >1 results in a many to many problem. The diagram comes from Andrei Karpathy's [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)*.\n",
        "\n",
        "Before we build a Conv1D model, let's recreate our datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_t9hXvXrZPD"
      },
      "source": [
        "HORIZON = 1 # predict next day\n",
        "WINDOW_SIZE = 7 # use previous week worth of data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRCy-vXzsEGL",
        "outputId": "09b79a4c-efd0-4e96-ae40-98ed0dba39fe"
      },
      "source": [
        "# Create windowed dataset\n",
        "full_windows, full_labels = make_windows(prices, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(full_windows), len(full_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2780, 2780)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtsLeB3rsED2",
        "outputId": "dcab650a-b245-4b00-d475-48bb8f5435fe"
      },
      "source": [
        "# Create train/test splits\n",
        "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(full_windows, full_labels)\n",
        "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2224, 556, 2224, 556)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMtzZKYRXExH"
      },
      "source": [
        "Data windowed!\n",
        "\n",
        "Now, since we're going to be using [Conv1D layers](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D), we need to make sure our input shapes are correct.\n",
        "\n",
        "The Conv1D layer in TensorFlow takes an input of: `(batch_size, timesteps, input_dim)`.\n",
        "\n",
        "In our case, the `batch_size` (by default this is 32 but we can change it) is handled for us but the other values will be:\n",
        "* `timesteps = WINDOW_SIZE` - the `timesteps` is also often referred to as `features`, our features are the previous `WINDOW_SIZE` values of Bitcoin \n",
        "* `input_dim = HORIZON` - our model views `WINDOW_SIZE` (one week) worth of data at a time to predict `HORIZON` (one day)\n",
        "\n",
        "Right now, our data has the `timesteps` dimension ready but we'll have to adjust it to have the `input_dim` dimension. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "To_k7zIAX7Pj",
        "outputId": "ee118751-d61f-43b0-b6a4-278acbf2b828"
      },
      "source": [
        "# Check data sample shapes\n",
        "train_windows[0].shape # returns (WINDOW_SIZE, )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7,)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPIMnb3GYMvP"
      },
      "source": [
        "To fix this, we could adjust the shape of all of our `train_windows` or we could use a [`tf.keras.layers.Lamdba`](https://keras.io/api/layers/core_layers/lambda/) (called a Lambda layer) to do this for us in our model.\n",
        "\n",
        "The Lambda layer wraps a function into a layer which can be used with a model.\n",
        "\n",
        "Let's try it out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62oSNy3VvZpv",
        "outputId": "d822a1bd-2cae-4a7c-a616-1ce82887303a"
      },
      "source": [
        "# Before we pass our data to the Conv1D layer, we have to reshape it in order to make sure it works\n",
        "x = tf.constant(train_windows[0])\n",
        "expand_dims_layer = layers.Lambda(lambda x: tf.expand_dims(x, axis=1)) # add an extra dimension for timesteps\n",
        "print(f\"Original shape: {x.shape}\") # (WINDOW_SIZE)\n",
        "print(f\"Expanded shape: {expand_dims_layer(x).shape}\") # (WINDOW_SIZE, input_dim) \n",
        "print(f\"Original values with expanded shape:\\n {expand_dims_layer(x)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original shape: (7,)\n",
            "Expanded shape: (7, 1)\n",
            "Original values with expanded shape:\n",
            " [[123.65499]\n",
            " [125.455  ]\n",
            " [108.58483]\n",
            " [118.67466]\n",
            " [121.33866]\n",
            " [120.65533]\n",
            " [121.795  ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKt2YFb5ip4n"
      },
      "source": [
        "Looking good!\n",
        "\n",
        "Now we've got a Lambda layer, let's build, compile, fit and evaluate a Conv1D model on our data.\n",
        "\n",
        "> 🔑 **Note:** If you run the model below without the Lambda layer, you'll get an input shape error (one of the most common errors when building neural networks)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jODevMWGsEBI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d264c36-4f20-4573-88e7-dd498870a6aa"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create model\n",
        "model_4 = tf.keras.Sequential([\n",
        "  # Create Lambda layer to reshape inputs, without this layer, the model will error\n",
        "  layers.Lambda(lambda x: tf.expand_dims(x, axis=1)), # resize the inputs to adjust for window size / Conv1D 3D input requirements\n",
        "  layers.Conv1D(filters=128, kernel_size=5, padding=\"causal\", activation=\"relu\"),\n",
        "  layers.Dense(HORIZON)\n",
        "], name=\"model_4_conv1D\")\n",
        "\n",
        "# Compile model\n",
        "model_4.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "\n",
        "# Fit model\n",
        "model_4.fit(train_windows,\n",
        "            train_labels,\n",
        "            batch_size=128, \n",
        "            epochs=100,\n",
        "            verbose=0,\n",
        "            validation_data=(test_windows, test_labels),\n",
        "            callbacks=[create_model_checkpoint(model_name=model_4.name)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/model_4_conv1D/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_4_conv1D/assets\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdcf1dfba50>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWqZR5cSjHCf"
      },
      "source": [
        "What does the Lambda layer look like in a summary?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FcGUALG4RxJ",
        "outputId": "945dbb97-927f-4f70-8a13-391c18db821d"
      },
      "source": [
        "model_4.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_4_conv1D\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda_1 (Lambda)            (None, 1, 7)              0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 1, 128)            4608      \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1, 1)              129       \n",
            "=================================================================\n",
            "Total params: 4,737\n",
            "Trainable params: 4,737\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIZEau4Vjk-M"
      },
      "source": [
        "The Lambda layer appears the same as any other regular layer. \n",
        "\n",
        "Time to evaluate the Conv1D model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXEL9FuhsD-3",
        "outputId": "315e48d9-5262-4dca-a7db-9b3f2d3c07aa"
      },
      "source": [
        "# Load in best performing Conv1D model and evaluate it on the test data\n",
        "model_4 = tf.keras.models.load_model(\"model_experiments/model_4_conv1D\")\n",
        "model_4.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 3ms/step - loss: 570.8283\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "570.8283081054688"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j99tbnwSsD8R",
        "outputId": "1cf57db2-3dff-4c14-8db8-e5dda6e29f4f"
      },
      "source": [
        "# Make predictions\n",
        "model_4_preds = make_preds(model_4, test_windows)\n",
        "model_4_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([8851.464, 8754.471, 8983.928, 8759.672, 8703.627, 8708.295,\n",
              "       8661.667, 8494.839, 8435.317, 8492.115], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjfEDWxjrZW2",
        "outputId": "abb0a047-5324-4e0a-a21e-f092f23d24a3"
      },
      "source": [
        "# Evaluate predictions\n",
        "model_4_results = evaluate_preds(y_true=tf.squeeze(test_labels),\n",
        "                                 y_pred=model_4_preds)\n",
        "model_4_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 570.8283,\n",
              " 'mape': 2.5593357,\n",
              " 'mase': 1.0027872,\n",
              " 'mse': 1176671.1,\n",
              " 'rmse': 1084.7448}"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZ2s0Rx0rZjE"
      },
      "source": [
        "## Model 5: RNN (LSTM)\n",
        "\n",
        "As you might've guessed, we can also use a recurrent neural network to model our sequential time series data.\n",
        "\n",
        "> 📖 **Resource:** For more on the different types of recurrent neural networks you can use for sequence problems, see the [Recurrent Neural Networks section of notebook 08](https://dev.mrdbourke.com/tensorflow-deep-learning/08_introduction_to_nlp_in_tensorflow/#recurrent-neural-networks-rnns).\n",
        "\n",
        "Let's reuse the same data we used for the Conv1D model, except this time we'll create an [LSTM-cell](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM) powered RNN to model our Bitcoin data. \n",
        "\n",
        "Once again, one of the most important steps for the LSTM model will be getting our data into the right shape.\n",
        "\n",
        "The [`tf.keras.layers.LSTM()`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM) layer takes a tensor with `[batch, timesteps, feature]` dimensions.\n",
        "\n",
        "As mentioned earlier, the `batch` dimension gets taken care of for us but our data is currently only has the `feature` dimension (`WINDOW_SIZE`).\n",
        "\n",
        "To fix this, just like we did with the `Conv1D` model, we can use a `tf.keras.layers.Lambda()` layer to adjust the shape of our input tensors to the LSTM layer.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6YFL_b-rZrt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96a90e42-069a-459a-b140-6191cab11eb1"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# Let's build an LSTM model with the Functional API\n",
        "inputs = layers.Input(shape=(WINDOW_SIZE))\n",
        "x = layers.Lambda(lambda x: tf.expand_dims(x, axis=1))(inputs) # expand input dimension to be compatible with LSTM\n",
        "# print(x.shape)\n",
        "# x = layers.LSTM(128, activation=\"relu\", return_sequences=True)(x) # this layer will error if the inputs are not the right shape\n",
        "x = layers.LSTM(128, activation=\"relu\")(x) # using the tanh loss function results in a massive error\n",
        "# print(x.shape)\n",
        "# Add another optional dense layer (you could add more of these to see if they improve model performance)\n",
        "# x = layers.Dense(32, activation=\"relu\")(x)\n",
        "output = layers.Dense(HORIZON)(x)\n",
        "model_5 = tf.keras.Model(inputs=inputs, outputs=output, name=\"model_5_lstm\")\n",
        "\n",
        "# Compile model\n",
        "model_5.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "\n",
        "# Seems when saving the model several warnings are appearing: https://github.com/tensorflow/tensorflow/issues/47554 \n",
        "model_5.fit(train_windows,\n",
        "            train_labels,\n",
        "            epochs=100,\n",
        "            verbose=0,\n",
        "            batch_size=128,\n",
        "            validation_data=(test_windows, test_labels),\n",
        "            callbacks=[create_model_checkpoint(model_name=model_5.name)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_5_lstm/assets\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdcf17b5190>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOpHqmEtSsNw",
        "outputId": "8071fad7-885c-40d6-a601-0dd333aab6e1"
      },
      "source": [
        "# Load in best version of model 5 and evaluate on the test data\n",
        "model_5 = tf.keras.models.load_model(\"model_experiments/model_5_lstm/\")\n",
        "model_5.evaluate(test_windows, test_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
            "18/18 [==============================] - 0s 2ms/step - loss: 596.6447\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "596.6446533203125"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pm_ZVNomnWvP"
      },
      "source": [
        "Now we've got the best performing LSTM model loaded in, let's make predictions with it and evaluate them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfG11v_51UWa",
        "outputId": "764915f3-48c2-4e64-b8a3-42867ea9eb0b"
      },
      "source": [
        "# Make predictions with our LSTM model\n",
        "model_5_preds = make_preds(model_5, test_windows)\n",
        "model_5_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([8991.225, 8823.2  , 9009.359, 8847.859, 8742.254, 8788.655,\n",
              "       8744.746, 8552.568, 8514.823, 8542.873], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVv5Kg5N1axK",
        "outputId": "ef27748e-7980-4e94-af5a-b9f89840840a"
      },
      "source": [
        "# Evaluate model 5 preds\n",
        "model_5_results = evaluate_preds(y_true=tf.squeeze(test_labels),\n",
        "                                 y_pred=model_5_preds)\n",
        "model_5_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 596.64465,\n",
              " 'mape': 2.6838453,\n",
              " 'mase': 1.0481395,\n",
              " 'mse': 1273486.9,\n",
              " 'rmse': 1128.4888}"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_chWnLWlb2O"
      },
      "source": [
        "Hmmm... it seems even with an LSTM-powered RNN we weren't able to beat our naïve models results.\n",
        "\n",
        "Perhaps adding another variable will help?\n",
        "\n",
        "> 🔑 **Note:** I'm putting this here again as a reminder that because neural networks are such powerful algorithms, they can be used for almost any problem, however, that doesn't mean they'll achieve performant or usable results. You're probably starting to clue onto this now.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y82CDMWp5Wva"
      },
      "source": [
        "## Make a multivariate time series \n",
        "\n",
        "So far all of our models have barely kept up with the naïve forecast.\n",
        "\n",
        "And so far all of them have been trained on a single variable (also called univariate time series): the historical price of Bitcoin.\n",
        "\n",
        "If predicting the price of Bitcoin using the price of Bitcoin hasn't worked out very well, maybe giving our model more information may help.\n",
        "\n",
        "More information is a vague term because we could actually feed almost anything to our model(s) and they would still try to find patterns. \n",
        "\n",
        "For example, we could use the historical price of Bitcoin as well as anyone with the name [Daniel Bourke Tweeted](https://twitter.com/mrdbourke) on that day to predict the future price of Bitcoin.\n",
        "\n",
        "But would this help?\n",
        "\n",
        "Porbably not.\n",
        "\n",
        "What would be better is if we passed our model something related to Bitcoin (again, this is quite vauge, since in an open system like a market, you could argue everything is related).\n",
        "\n",
        "This will be different for almost every time series you work on but in our case, we could try to see if the [Bitcoin block reward size](https://www.investopedia.com/terms/b/block-reward.asp) adds any predictive power to our model(s).\n",
        "\n",
        "What is the Bitcoin block reward size?\n",
        "\n",
        "The Bitcoin block reward size is the number of Bitcoin someone receives from mining a Bitcoin block.\n",
        "\n",
        "At its inception, the Bitcoin block reward size was 50.\n",
        "\n",
        "But every four years or so, the Bitcoin block reward halves.\n",
        "\n",
        "For example, the block reward size went from 50 (starting January 2009) to 25 on November 28 2012.\n",
        "\n",
        "Let's encode this information into our time series data and see if it helps a model's performance.\n",
        "\n",
        "> 🔑 **Note:** Adding an extra feature to our dataset such as the Bitcoin block reward size will take our data from **univariate** (only the historical price of Bitcoin) to **multivariate** (the price of Bitcoin as well as the block reward size)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "Z06o2ls4sSoF",
        "outputId": "f3e41f79-ead0-415a-f2a5-ec0574767e27"
      },
      "source": [
        "# Let's make a multivariate time series\n",
        "bitcoin_prices.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>123.65499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>125.45500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>108.58483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>118.67466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>121.33866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Price\n",
              "Date                 \n",
              "2013-10-01  123.65499\n",
              "2013-10-02  125.45500\n",
              "2013-10-03  108.58483\n",
              "2013-10-04  118.67466\n",
              "2013-10-05  121.33866"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEqgTKX-t7xe"
      },
      "source": [
        "Alright, time to add another feature column, the block reward size.\n",
        "\n",
        "First, we'll need to create variables for the different block reward sizes as well as the dates they came into play.\n",
        "\n",
        "The following block rewards and dates were sourced from [cmcmarkets.com](https://www.cmcmarkets.com/en/learn-cryptocurrencies/bitcoin-halving).\n",
        "\n",
        "| Block Reward | Start Date |\n",
        "| ----- | ----- |\n",
        "| 50 | 3 January 2009 (2009-01-03) |\n",
        "| 25 | 28 November 2012 |\n",
        "| 12.5 | 9 July 2016 |\n",
        "| 6.25 | 11 May 2020 |\n",
        "| 3.125 | TBA (expected 2024) |\n",
        "| 1.5625 | TBA (expected 2028) | \n",
        "\n",
        "> 🔑 **Note:** Since our Bitcoin historical data starts from 01 October 2013, none of the timesteps in our multivariate time series will have a block reward of 50."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrUKQXnssWHt"
      },
      "source": [
        "# Block reward values\n",
        "block_reward_1 = 50 # 3 January 2009 (2009-01-03) - this block reward isn't in our dataset (it starts from 01 October 2013)\n",
        "block_reward_2 = 25 # 28 November 2012 \n",
        "block_reward_3 = 12.5 # 9 July 2016\n",
        "block_reward_4 = 6.25 # 11 May 2020\n",
        "\n",
        "# Block reward dates (datetime form of the above date stamps)\n",
        "block_reward_2_datetime = np.datetime64(\"2012-11-28\")\n",
        "block_reward_3_datetime = np.datetime64(\"2016-07-09\")\n",
        "block_reward_4_datetime = np.datetime64(\"2020-05-11\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2RiTXPd3lAL"
      },
      "source": [
        "We're going to get the days (indexes) for different block reward values. \n",
        "\n",
        "This is important because if we're going to use multiple variables for our time series, they have to the same frequency as our original variable. For example, if our Bitcoin prices are daily, we need the block reward values to be daily as well.\n",
        "\n",
        "> 🔑 **Note:** For using multiple variables, make sure they're the same frequency as each other. If your variables aren't at the same frequency (e.g. Bitcoin prices are daily but block rewards are weekly), you may need to transform them in a way that they can be used with your model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmawWFj9udzC",
        "outputId": "c2796842-b9fa-4d12-9005-1534216a2cd5"
      },
      "source": [
        "# Get date indexes for when to add in different block dates\n",
        "block_reward_2_days = (block_reward_3_datetime - bitcoin_prices.index[0]).days\n",
        "block_reward_3_days = (block_reward_4_datetime - bitcoin_prices.index[0]).days\n",
        "block_reward_2_days, block_reward_3_days"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1012, 2414)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2ZxThVAy0B3"
      },
      "source": [
        "Now we can add another feature to our dataset `block_reward` (this gets lower over time so it may lead to increasing prices of Bitcoin)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "BM9zRZDQvw7m",
        "outputId": "aee83cc1-58e7-4521-a6d6-c6d37e9dbffa"
      },
      "source": [
        "# Add block_reward column\n",
        "bitcoin_prices_block = bitcoin_prices.copy()\n",
        "bitcoin_prices_block[\"block_reward\"] = None\n",
        "\n",
        "# Set values of block_reward column (it's the last column hence -1 indexing on iloc)\n",
        "bitcoin_prices_block.iloc[:block_reward_2_days, -1] = block_reward_2\n",
        "bitcoin_prices_block.iloc[block_reward_2_days:block_reward_3_days, -1] = block_reward_3\n",
        "bitcoin_prices_block.iloc[block_reward_3_days:, -1] = block_reward_4\n",
        "bitcoin_prices_block.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>block_reward</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>123.65499</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>125.45500</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>108.58483</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>118.67466</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>121.33866</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Price block_reward\n",
              "Date                              \n",
              "2013-10-01  123.65499           25\n",
              "2013-10-02  125.45500           25\n",
              "2013-10-03  108.58483           25\n",
              "2013-10-04  118.67466           25\n",
              "2013-10-05  121.33866           25"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZuC3pvWwfXq"
      },
      "source": [
        "Woohoo! We've officially added another variable to our time series data.\n",
        "\n",
        "Let's see what it looks like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "Tfh8dmtF46Lf",
        "outputId": "f0cfd814-564f-47c9-a3c5-a77272e6c12c"
      },
      "source": [
        "# Plot the block reward/price over time\n",
        "# Note: Because of the different scales of our values we'll scale them to be between 0 and 1.\n",
        "from sklearn.preprocessing import minmax_scale\n",
        "scaled_price_block_df = pd.DataFrame(minmax_scale(bitcoin_prices_block[[\"Price\", \"block_reward\"]]), # we need to scale the data first\n",
        "                                     columns=bitcoin_prices_block.columns,\n",
        "                                     index=bitcoin_prices_block.index)\n",
        "scaled_price_block_df.plot(figsize=(10, 7));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SQ55nMEVERETdimdrvrgl+B+ZOzLkeQZTRFrHpp1ERFFRfmrGKzMVDoMpIiIi6lZcXfxLKIMpIs1iAToRUYeoHdCZmSIiIiKKmkvdm69rMJgiIiKiboU1U0REREQxUDNTXZSaYjBFpFVs2klE1CEyztvJhMNgioiIiLoVdTuZLqqaYjBFRERE3UpbzVTXvB+DKSKtY9NOIqKouFzx3eg4HAZTRERE1K0wM0VEHixAJyIKpcXmwKGq5nbHXUoBOmumiIiIiIK7e+EWXPD0l7A7XT7H1Wm+LopyGEwRaR5rpoiIAll3qBoAcLy21ef4bxfvBsAO6EREREQhmQx6AECjxRHwPDugE/V0bNpJRBSSw+We3rP5TfMp2LSTiIiIKAS7wx1E+ddMKZiZIiIiIopAsGCqqzCYItI6Nu0kIgpI52kkFSyY4jQfERERUQjKNJ7NEfiXTk7zEREREYWgdDgPmpnqqnF00fsQERERxZWSeWIBOhGFwZopIqJAkqpmSggxUwixVwixXwgxL8g11wghdgkhdgoh3orvMImIiIh8Ketz7nt3O840WtqdF1rJTAkh9ACeAzALwAgA1wshRvhdMxjA/QCmSClHAvhpJ4yVqGdh004iopCk12rnL/dUqo/TTXr0yTZ32TgiyUxNArBfSnlQSmkD8DaAK/yuuQ3Ac1LKWgCQUp6J7zCJiIiIfEmfx23PslONmDqooMvGEUkw1Q/AMa/nFZ5j3oYAGCKEWCmEWCOEmBnoRkKI24UQG4QQGyorKwNdQkRERBQR78yUd0s+h0vCoO+67H68CtANAAYDmAHgegAvCSFy/C+SUr4opSyXUpYXFhbG6a2Jujk27SQiCkgGeWyxO5Hi2QS5K0QSTB0HUOL1vNhzzFsFgA+llHYp5SEA++AOroiIiIg6hffvmi7PE5dLosHiQFaqscvGEUkwtR7AYCFEmRDCBOA6AB/6XfM+3FkpCCEK4J72OxjHcRL1QCxAJyIKxRVgmu/dTRUAgC3H6rpsHGGDKSmlA8CdAD4GsBvAO1LKnUKI3wgh5nou+xhAtRBiF4AvAPxCSlndWYMmIiIi8p7bUx42Wx0AgPKzcrtsGIZILpJSLgaw2O/Yw16PJYB7Pf8RUVyxZoqIKBCfn46e1FRuugkAMHt0ny4bBzugExERUVLyWc3n+b/D6X5kTMLVfEQUb2zaSUQUkndm6uEPdsJid8Lhcm8tY9B3XYjDYIqIiIiSksuvdczyr6tgVzJTOmamiIiIiELyb8OXm2aEw8nMFBH5Y9NOIqKA/DNTDpeEw+U+lowd0ImIiIi6lNPlG0zZnS41mDLqmJkiIjbtJCIKSkoJv1jKHUyp03zMTBEREREF5Z+VAgCbQ6oF6AYWoBNRG9ZMERH5cwaoJ3VP87lg0AmILmwvw2CKiIiIkk6gzJR7mk9C34VZKYDBFJF2sWknEVFQgaf5XDhY1QxjF7ZFACLcm4+IiIhISzyNzn18sOUEVh+s7vKxaCqYstvtqKiogMViSfRQyMNsNqO4uBhGozHRQyEiIlI5AkRTh6ubEzASjQVTFRUVyMzMRGlpaZcWjlFgUkpUV1ejoqICZWVliR5Oz8WmnURE7QQqQE816RMwEo3VTFksFuTn5zOQ0gghBPLz85kpJCIizQlUM5Woxc+aCqYAMJDSGP55JBK/90REwSjB1MXDi/DruSMTOhbNBVNERERE4SglU7NG9cGV4/sBSFxXPgZTfvR6PcaNG4dRo0bh6quvRktLS8DrzjvvvC4eGfVcrJkiIvKnFKDrdQJKWyn/jY+7CoMpP6mpqdiyZQt27NgBk8mEBQsW+Jx3OBwAgFWrViVieERERIS2wMkdTLmjqUaLIyFj0dRqPm+//s9O7DrRENd7juibhV99I/J51WnTpmHbtm348ssv8dBDDyE3Nxd79uzBvn37kJGRgaamJgDAk08+iTfeeAM6nQ6zZs3C/PnzceDAAdxxxx2orKxEWloaXnrpJQwbNiyuXw91c6xXIyIKyuFqH0zVNNsSMhbNBlOJ5nA4sGTJEsycORMAsGnTJuzYsaNdi4AlS5bggw8+wNq1a5GWloaamhoAwO23344FCxZg8ODBWLt2LX7yk5/g888/7/Kvg4iIqDuyOdzTfDohEv67p2aDqWgySPHU2tqKcePGAXBnpm699VasWrUKkyZNCthraenSpbjllluQlpYGAMjLy0NTUxNWrVqFq6++Wr3OarV2zRdARETUA/zsH1sAAAavzJTisStHdelYNBtMJYpSM+UvPT094nu4XC7k5OQEvA9R1Ni0k4ionQOV7m7nOh3abWx8w6T+XToWFqDH6JJLLsFrr72mrvqrqalBVlYWysrK8M9//hOAu5P41q1bEzlMIiKibsnlAvxiKej8D3QyBlMxmjlzJubOnYvy8nKMGzcOTz/9NADgzTffxCuvvIKxY8di5MiR+OCDDxI8Uko+LEAnIgrHKWXCG0xzms+PskLP24wZMzBjxoyg182bNw/z5s3zOV9WVoaPPvqoU8ZIREREbq5A28p0MWamiDQv8T8oiIi0SgOxFIMpIiIiSl5ODSzSYTBFpFWJbpxCRJQEOM1HREREFAMngykiIiKi6KUa9QCA6UMKEzwSruYj0j4N1AMQEWnNkN6ZyEk1ojAzJdFDYWbK3+HDhzFqVPs29DNmzMCGDRuivt8jjzyi9p7SutLSUlRVVSV6GERERGE5Xa52nc8ThcGURjkcjqS+P8WDNn5IEBFpkdOFdnvyJYp2p/mWzANObY/vPXuPBmbND3uZw+HAjTfeiE2bNmHkyJF4/fXXfc4vXLgQjz/+OKSUmDNnDp588kkAwEcffYQHHngATqcTBQUF+Oyzz3xe99JLL+G9997De++9h9TU1HbvO2PGDIwbNw4rVqzA9ddfjxkzZuDee+9FU1MTCgoK8Ne//hV6vR6zZs3Cxo0bsXXrVowbNw5HjhxB//79MXDgQGzfvh2fffYZHnvsMdhsNuTn5+PNN99Er1698Mgjj+DAgQM4ePAg+vfvj2effRbXX389jh8/jsmTJ0NyOomIiJKEyyVh0EhmSrvBVALt3bsXr7zyCqZMmYLvf//7eP7559VzJ06cwH333YeNGzciNzcXl156Kd5//31MmTIFt912G5YtW4aysjLU1NT43PPZZ5/Fp59+ivfffx8pKcHnd202GzZs2AC73Y7zzz8fH3zwAQoLC/GPf/wDv/zlL/Hqq6/CYrGgoaEBy5cvR3l5OZYvX46pU6eiqKgIaWlpmDp1KtasWQMhBF5++WX87ne/w+9//3sAwK5du7BixQqkpqbi7rvvxtSpU/Hwww9j0aJFeOWVVzrnG0oxYpBLROTP4TfN98EdU/DxzlMwewrTu5J2g6kIMkidpaSkBFOmTAEA3HTTTXjmmWfUc+vXr8eMGTNQWOhePXDjjTdi2bJl0Ov1mD59OsrKygAAeXl56mtef/11lJSU4P3334fRaAz53tdeey0Ad0C3Y8cOXHLJJQAAp9OJPn36AADOO+88rFy5EsuWLcMDDzyAjz76CFJKTJs2DQBQUVGBa6+9FidPnoTNZlPHBABz585Vs2LLli3De++9BwCYM2cOcnNzO/gdIyIi6lou6buh8diSHIwtyUnIWFgzFYD/homxbqA4evRoHD58GBUVFWGvTU9PBwBIKTFy5Ehs2bIFW7Zswfbt2/HJJ58AAKZPn47ly5fjyJEjuOKKK7B161asWLFCDabuuusu3Hnnndi+fTteeOEFWCyWdvenJKCRWgAiIi1yuiT0GvkxyWAqgKNHj2L16tUAgLfeegtTp05Vz02aNAlfffUVqqqq4HQ6sXDhQpx//vk499xzsWzZMhw6dAgAfKb5xo8fjxdeeAFz587FiRMnIhrD0KFDUVlZqY7Dbrdj586dAIBp06bhjTfewODBg6HT6ZCXl4fFixer46yvr0e/fv0AAH/729+Cvsf06dPx1ltvAQCWLFmC2traiMZGRESUaE6XhF6njTBGG6PQmKFDh+K5557D8OHDUVtbix//+MfquT59+mD+/Pm44IILMHbsWEycOBFXXHEFCgsL8eKLL+Kqq67C2LFj1ek6xdSpU/H0009jzpw5EbUfMJlM+Ne//oX77rsPY8eOxbhx47Bq1SoA7hYGUkpMnz5dvXdOTo46TffII4/g6quvxsSJE1FQUBD0PX71q19h2bJlGDlyJN577z30798/6u8VERFRIriDqUSPwk0kagVXeXm59O/btHv3bgwfPjwh46Hg+OeSIA4r8FgRcOFDwPT/SfRoiIg05ezfLsXFw4vwxFVjuuT9hBAbpZTlgc5pJKYjIiIiipzd6YJBI9N82l3N143dcccdWLlypc+xe+65B7fcckuCRkTapJHKSiIijWm1OVHXYkevrMRvJQNoMJiSUsa8ek7rnnvuuUQPIWJs5ElERFpzrLYFAFCSl5bgkbhpIz/mYTabUV1dzQ9wjZBSorq6GmazOdFD6eH474GIyNvx2lYAQHGuNoIpTWWmiouLUVFRgcrKykQPhTzMZjOKi4sTPQwiIiJVs829v2ymWRthjDZG4WE0Gn26dRP1aN18upuIqKMcTnfGXit782lqmo+IiIgoHLvTBQAwaqTRlDZGQURERBQhuyczxWCKiCLD+nMiIh8OlzszZdDI5nwMpoiIiCipqJkpjTTt1MYoiCgAbfzGRUSkNQ4nM1NEREREHWZnMEVE0WHRFBGRN07zEREREcXA6nDBqBfQsc8UEYXEpp1ERAGdrG9FryztbHXGYIqIiIiSSkVtK0o0si8fwGCKiIiIkkiz1YGNR2rRJ5uZKSKKlGQBOhGRoqrJCgA4Kz89wSNpw2CKiIiIkoaykq+0IMmm+YQQM4UQe4UQ+4UQ80Jc9y0hhBRClMdviEQ9FQvQiYj8KVvJaGVfPiCCYEoIoQfwHIBZAEYAuF4IMSLAdZkA7gGwNt6DJCIiIgIAhyczZdBIWwQgsszUJAD7pZQHpZQ2AG8DuCLAdY8CeBKAJY7jIyI27SQiUindz5MqMwWgH4BjXs8rPMdUQogJAEqklItC3UgIcbsQYoMQYkNlZWXUgyUiIqKeTe1+nmTBVEhCCB2APwD4ebhrpZQvSinLpZTlhYWFsb41UffGpp1ERO1obZNjILJg6jiAEq/nxZ5jikwAowB8KYQ4DOBcAB+yCJ2IiIjize5SMlPJFUytBzBYCFEmhDABuA7Ah8pJKWW9lLJASlkqpSwFsAbAXCnlhk4ZMREREfVYamZKI5scAxEEU1JKB4A7AXwMYDeAd6SUO4UQvxFCzO3sARL1eGzaSUSkUmqmtDTNZ4jkIinlYgCL/Y49HOTaGbEPi4iIiKg9q8MJAEg16hM8kjbayZERkS8WoBMRtdNq8wRTJgZTRERERFFptNixtaIegLYyUxFN8xFRIrFmiogIAG5/fSNWH6wGAJg1FEwxM0VERERJYf3hGvVxikE7IYx2RkJEREQUQlaqUX0sNFRXymCKiIiIkoJZQ9kob9ocFREREZGf3tlmAMCoflkJHokvBlNEWsemnUREAIBGiwPnDynEf++aluih+GAwRUREREnhdIMFZQXpiR5GOwymiDRNOwWWRESJZndKTW1wrGAwRUREREnB4XLBqNde6KK9ERGRH9ZMERFJKWF3ShgYTBERERFFz+Fy/2Jp1HGaj4iioaGmdEREieRweoIpDfaa0t6IiIiIiPzYnC4AgIGZKSIiIqLoOTzBlEmDmSlDogdARGEc+BywtSR6FETUHWUUAlN+mhQlBXbPNJ9Bx2CKiKJRfDZwehdQuS/RIyGi7sZpA5xWYPQ1QHa/RI8mLLsnM6XFPlMMpoi07NZPEj0CIuquNr8BfHAHIF2JHklE2oIp7WWmtDciIiIi6kLJ0ctObY3AYIqIiIgoekpmyqDBaT4GU0RERD2SJyiRyZGZUgrQTcxMEREREUXPwcwUERERaYraDiE5MlNtTTu1F7pob0REREREfpTtZEwG7WWm2BqBiIioR9JeUBKIMr1nZ2aKiIiINEnDBeg1zTYM+uUSnPP4Z7A62GeKiKhbOlDZhDUHqxM9DKJu6Q+f7gUAVDfbsOdUIwCgJC81kWAgOEMAACAASURBVEMKiNN8REQxuOj3XwEA9v92Fgwa/I2ZKCiNF6B/ta8Sb6w5qj5/5rOvUVaQjkyzMYGjCoz/8omI4mDh+mOJHgJRt3Lzq+vaHRvZNysBIwmPwRQRURxY7c5ED4EoStpt2ml1BP731Cfb3MUjiQyDKSKiOOibo706DqJk9fnuMz7P54zuAwBINWmzOonBFBFRDAoyUgAAuuRYZU7URmj3L21ti93n+ZjibABAZaMlEcMJi8EUEVFM3FMkyr5hRBS7JqtvMDW0dyYAwGJ3JWI4YWkzX0ZElCSUchOni8EUJRvtZqYaLQ6f59MGF+LnlwzBtZNKEjSi0BhMERHFQAmhlO7MRElHgwXo+880+TzX6wTuumhwgkYTHqf5iIhiID0fRMxMEcXPjhP1GFCQnuhhRIzBFBFRDJQgys5gipKNhpt2VjXa0D8/LdHDiBiDKSKiDnK6JBo8tR1OTvMRxYXLJdFqdyI7VXudzoNhMEVE1EFN1rYi2fpWR4griTRMYzVTrZ4GuP3z3JmpZGg7wmCKiKiDvLue/3HpPgDu36qrm6yJGhJR0muxuf9dFWWm4H8uHYK3bjs3wSMKj8EUEVEHLfXq0jykVwYA4NFFuzDxsaU+WSsiTdJozVSrJ5hKNRlw54WDce6A/ASPKDwGU0REHdBic+CBf29XnytTEq+tPAwAaGYwRdQhjZ6GnWkmfYJHEjkGU0REHfC//9rm89y/NYKFGx8TdcicZ1YAAFIZTBERdW97TzWqj9NMevjvJqPVbS+I2nim+TRWgK5I1+imxoEkz0iJiDREmYIozU9DfkYKqhqtOFrdop63OpiZIopWi61tejzFkDz5nuQZKRGRhpiN7mDK5nBBrxPYdbIB05/6Ql3GzcwUaZ4GC9Crm2zq4yG9MhM4kugwmCIi6gCT57dmh0tCL9oa4eg90ZT3b9hEFJ7N4cK3F6wCALxyczlrpoiIegoh2gIoAMg0u7s21zTbgr2ESCO0VTP14dYTON3g7tGWl25K8Giiw2CKiCgGUvoGU/Wt7mXdVWzcSRQV73YiA4syEjiS6DGYIiKKgYRvMKW0SGiysgCdkoU2MlPe/46yzMmzLx/AYIqIKGb6AJuHWdlnirROaGvTO5dGphs7gsEUEVEMpAQ+3XW63fFWBlNEUbE53Ctgb51aluCRRI/BFBFRTAL/Ns0O6KR92ipAt3qCqV9cNjTBI4kegykiok7w5d5K1LfYEz0MoqShBFPJ1KxTkXwjJiLSkGC/1J9ptOLOhZu6djBE0dBY006r3QmTQQehsVquSDCYIiKKgQTw+vcnBTx3sLK5awdDlKSqm6x4YdlBZKcm1yo+BYMpIqIY9c42Bzyeaeb2p6Rl2qiZcjhdmPjYUgBAZWNy9mdjMEVE1AHKMm4pJTJSAgdNDKaIwttwpFZ9fP6QwgSOpOMYTBERdYDd2fbbfFqQPcSUzZCJtC2xmSnvPm3P3zghgSPpuIiCKSHETCHEXiHEfiHEvADn7xVC7BJCbBNCfCaEOCv+QyUi0g67073ySALISTPh13NHqucGFKQDAFptbI9AGqaBQm+bw4WnP94LAEg36ZEeJMurdWGDKSGEHsBzAGYBGAHgeiHECL/LNgMol1KOAfAvAL+L90CJiLQk1ZN1mn/VGADAzeeVquc+vGsqJvTPQZPXXmNE1N6SHSex9lANAODt2ycneDQdF0kIOAnAfinlQQAQQrwN4AoAu5QLpJRfeF2/BsBN8RwkEZHWOF0Sk8ryMHNUb/XYpz+bjrQUAzJSDCjNT1c/JIi0KfEF6ErXcwAY2TcrYeOIVSTTfP0AHPN6XuE5FsytAJYEOiGEuF0IsUEIsaGysjLyURIRaYzd6WrXXHBwr0z0y0kFABj1OjhcrkAvJQ2qa7Fh2T5+LnU1m2e6fO0DF0EXYI/LZBHXAnQhxE0AygE8Fei8lPJFKWW5lLK8sDA5K/aJiAB3AbpRH/xHqF4vcLrBiiufW9mFo6JouFwSv/nPLhyobMLNr67Dd19d17Pq3OLctLPV5sR/tp6AjCLTZbW7gymzIbkXa0QyzXccQInX82LPMR9CiIsB/BLA+VLK5GwUQUQUIZvDBaM++G/SRs9v2VuO1XXVkChKByqb8OrKQ3h15SH1WIPFjtQgqzMptN99vAevrTyMwswUnDsgP6LXqFvIGJO7uUAko18PYLAQokwIYQJwHYAPvS8QQowH8AKAuVLKM/EfJhGRtjTbHGoReiCGEFkr0garo/00bKOl/X6K0WRaklKcvr7TDRYA7q2UAHfmLxyrw50JNCX5v5ewo5dSOgDcCeBjALsBvCOl3CmE+I0QYq7nsqcAZAD4pxBiixDiwyC3IyLqFqqbbCjISAl63uBV/xHJhwp1PaW9hbczfh24r3txNcruX9xNA6r41CjZnS5sr6jH4u2nAADbK+rwwZbjGPDAYhyvaw35WovdneFN5nopILJpPkgpFwNY7HfsYa/HF8d5XEREmtVic6DV7kR+qGDKawrwTKM16JYzlDit9vb1UTe8tBaH588BANS32LHmoHtF5uZjdZjQP7dLx9d1Oh4ozl+yBwu+OuBz7KXlbdOmhyqb1UUZ/v677QT2n2lCn+zA55NJcufViIgSoLrJBgAoyDAFvUava/vxeu4Tn/lkp6SU6n83vrwGS3ed7rzBUlDN1tDF5k22tj5hqw9Ud/Zwul4cmnb6B1L+bM7A3+Otx+pw51ubsXT3aQwsTI95HInGYIqIKEqVTe6poFDTfP5TSP+3dJ/6+Id/34iy+xfjcHULVu6vxg9e39A5A6WQmsM0VfVe2feUp0s3hbbknmk+z2ub29egAcDHO0+pj0sLGEwREfU4SmYqP0Rm6uMdp3yee2/m+oknE/XTf2zphNFRpMJ1qO/+bRKUpp0de/XTfgHmzZPPwvA+WbhlSql6rLbF/W/F6ZI+2dkj1S0A3DsJfHdyKZIdgykioiidqncX1YbKTPkvr7cFWDmmZEa6wzRHMgqWmfrFP7ei0WJHi2earyQvFelsl9DOs1/sVx8vunsqfn3FKADAr74xEoeemA29TqC2xYYNh2sw8IHFuPD3X8Llck9vV9S1YuqgAux+dCbKmJkiIup5Nh+rQ6bZgKLM4MHU0N6ZPs9z091ZLIfX9N/+M00AAH2Sr2RKVl97vv/+/rmxAq+vPoJTnqX+o/tlo9nmxM2vrsPGI+6C9BZbN9h3Mcamnb2z3IsqNjx4MUb2zfa7tUBumhE1zXa8tfYoAOBwdQuGPfQRfvC3Ddh6rA4DutEvEQymiIiitOVYHXLTTCF7ST125Sj880eTcf+sYchJM8LiWTlW3Wxrd219a+C6EupcJ+tbkebJOBX6BcbNVgcOnGmCTgBjinMAAF/tq8Tb645h89FajHj4Y7y8/GCXj1lLJg/MR/+8tKAZ2tw0E+pabKjy+jtvc7rw2R53O8rutMI1otYIRETkVttsw8HK5rDXpZkMOLs0D2eX5uGLvWfUbTPONLTfIKL71+ZoU02zHf3z0rDnVCMKM1Lgckk12LXYXThW29IuWNh4pBblpe4WCY8t2o0j1S14+BsjQm4t1Bn2nW7E16ebMGdMn9hv1sEeWlaHs93+lN5y00xY4lc76O0bY/p26H21iMEUEVEUjta0RP0as1GPGs+H9JlGS7vzgfodUeerbbZh2uACzBrVB1dN6IeT9RZc88JqAO5pvB3H63FOWZ5PV/SDVc24793t6vO/rzmCv685gpXzLgzaT6kzXPrHZQCA9JSzMWNoUQfv0n56+bkv9iPFoMMPpg0I+2qr3QVTiGBq54l69XFJXiqO1bhrDfc8OhMmvS7pG3V64zQfEVEUagJM04VjNuhxvNb9QVLp12F7XEkO7E4ZsBs3dR4pJWpabMjLMOGeiwejJC8NuWlG9fzRmhZUNVnROzsV0wYXhr3flPmfd+ZwfXhPL37vtfVxuGNbZuqpj/fisUW7I3qVzekKmZlq9sq4vvuj8/DzS4bgnR9Ohtmo71aBFMBgiogoKg0B9m4L5/O9Z1DdbEOz1dGuPqo0Pw0A0BLhVN/ukw1Yub8q6jGQrxabEzaHC7lpbe0tvKfqVh2oht0pUZBhwqCiDByePwd3XzRYPZ9lNuA3V4zEi9+ZqB6ramo/hdsZ/IOd2zvYp+zDbSeCnnv4gx3YXlEf9Dzgnp5OMQRf5fin68YBAH73rTEoyjLjrosGY1JZXofGqnUMpoiIotCRYvErx7lrQxotjnbL8Ys8K6IsEU71zfrTctz48tqox0C+lD34Cr3qoQJNWXkXpt97yRDcP2sYACDF0x/p0pG91aBByT52pkB7BH6y67TPKtFI/WtjBQDgqudXYub/LfP5u/366iN48IMd2HG8Hm+sORJwHBuO1KJviKnNK8b1w+H5c3DN2SVRjy3ZMJgiIorC3lONAIDnb5wQ8WvOKcsH4O411Wh1ICPFgLEl7hViJbnuD6NIarH2n2lUH3Pz5NicqnfXrnmvKDMEmHoq9FuplpbiLjVW2gIAUGulAgXa728+jtJ5i3CsA7V2/jYfrcXEx5YCAB7/5mifcyfq2tfihSO9aqb2nGrEDS+t8Tm/9VgdLv/zCjz4/g6cqGtFq82JRdtOAgBe8kw1DirKiPp9uyMGU0REUahstGJY70zMHh35KqoUo/tHrdXhRLPVgfQUPZ6/cQL+9aPJKMx0fyhfvWB1wKyD4sdvbMTFf1imPr9r4eYOfgUEAL/411YAQHFuW2YlJ82E4txULLhpIvI8fcEmnOW7ufGMIYUoyUvFU1ePUY+Zje6prkDZxfc2HwcATPvdF7juxdUdyiApHv5gp1qzN7JvFvY9NgsLbnJPMx6pCb/C1FugYHzniYag1+860YAbXl6DO97ahB/9fSNW7nfvVXjTuf2jet/uisEUEVEUmm3uzFI0TJ5anF++vwPNVifSUwzol5OK8tI8n4ad/958PGjGyX+J+aLtJ6McOSmklKjwTMl5T1OZDDqsuO9CzBzVG5/dez72PDpTDZQUJXlpWP6/F2JY7yz1mNkTLFsCdLnv65X5WnOwBh9uDV6nFI73xtp56SaYDDoMKnI3vjxe24rnvtjvs/IwFO/aP+HXtHP3b2a2y9L94PUN2Hy0DgDw0c5T+GpfJeaO7YtMsxHE1ghERFFpsjiQkxZ8T75AUjwfyOsO1cBk0GG4V3d07w+/e9/ZiscX78GGBy/2eX19C5t6xlNVU9uKzGD9oZSO9ZFQirADZab8Fyzc+85WXDWhOOJ7A8B/t51Av5xUHKxqxtjibMwYWqRm1LI8wcy899ztGrJSjfjOuWeFvefzXx7wmeZTjOqXhVSTHtseuRQCAga9wGV/XIaDVW2Zr8LMFJTlp+PWqWVRfR3dGYMpIqIoNFodKM5Li+o1Jq8PbJvDhdNejTtnjeqDe9/Zqj6varLivU0VPh+4v/t4j/p49uje2H+mCftON6GqyRpyf0Byszlc+Nk7WzCiTxZmjeqtZqV+MmNgXO6vZK+sAYKpFpsTY4qz8dr3zlbrnU43WNArK7Lu339ffRgPfbATgHv3lysuHIyfXTJEPe+fGVL2jQzlaHULXlx2EFM9fy0FJJbeez4u/sNX6t+nNFNbePDaLWdj/5kmFGWaUVqQxmxUAJzmIyKKQrPVgQxTdL+H+m96rOz5FugcAKz42rf1gbKH3K/njsTzN07ExcN7AQDKH1vK/lQR2FZRh0XbTuKpj/di9jPL1UUEt0XQmDISypY0Sl+lV1YcwobDyh5+TqQa9chLN0GZObvq+VURLyBY8FVbTykpgbIC30DebNT5TDu32sL/fVjhaa3xbU/ArtcJDCrKwLM3jMejns2KvZ2Vn46LhvfC6OJsBlJBMJgiIopCk8WBDHN0wVSe37TgI98Y4fP8nR9O9nnuPaUCAEa9wIT+Obj5vFIA7qkcxeBfLglZuO7P5nChtgONR5OZd/BqsbtwqsGCNJM+qqm8UNJMemSmGPD16SZ8fboRj/53F769YDWsDidabU6kmfQQQuDr385GltmA43Wt+GpfZUT3Ht4ny+d5/zzfzYGFEHjzB+eoz/1bbwSidNy/eGRvAMCvPH8fLx/TFyVRZl3JjcEUEVGEjtW0oNnmLiCPRl6G74f2oKJMn+eD/ZaXe/c7arDYsflonbq6DADOG5jvc71/V/VQnvxoD8Y/+il+7jW12F01WOz4y5cH8M6GCvVYdqoRlY3xnR4VQsBs0uPdTRW45I9tKy6veWENth+vV7OPep3AS98tB+Ab4IVysLIJ4/vnqM8HFKS3u2ZsSQ4OPD4bAwrS0WwLH0wptV3K37MRfgEbRY/BFBFRhK54biUAhNxCI5CMFIM6FQQAWam+wZjR737ehcyX/mEZWmxOnJXf9iE6pjjH5/oT9ZH3GHp15SEAwLubKnC8rvObTCbSbX/bgCc/2oNlniyQUS9Q32rHh1tPYHifzDCvjk6ggHbrMffqN++auYln5SIv3YSPQmwArPjWX1bhYFUzrHYXDj0xGxsfvDhoNk2vE0hPMWBXiPYGCovdCSEAo44hQLzwO0lEFIGfv7NV7fFzy5TSqF/v3ZfKe1k94Pth2y8nFdsq6lHTbENNs03NYJTk+naaXnT3VPXxqSiCKe8P0NMRZkeS1dpDNerjFIMON57TtsotN8oVmeEsunsqJpW2bZVy4bC2zYetXi0TDHodZo3qja0VdSHvV99qx8YjtQCAB2YPhxAC+WGyaduP1+NgVTPWeX3dgVQ12SClO6MGwF2MRTFhMEVEFIF3N7mniq4pL/ZZ6RQpm+cD9eHLR7TbtsT7+UDPlN/6wzVq1+xryotxo99y95F9s7Hm/osAuKcfH/lwJ0rnLcKmo7UhxzG2JFt9XN3Uc2qnRvXLVjNUADDXs8VPvIzsm413fjQZD10+AtedXYJXbi7Htye6C7ydfsXmhZkpqGux466Fm/G7j/YEup1PgDx1cEFEY/i5Z5VfuM24F647CtG99hlOOAZTRERR+J/Lhnbodcqqu7QAq/cAYOFt52JQUQb+13N/i92p1r98c3xxwH5IRZkpyDQb8NvFu/HXVYcBAA+9vyPoGHYcr8f6w7WYMbQQJr0Or6w42O6Dvrt67oYJuG162+q9Cf1zQ1zdcbdOLcP8b42BEAKXjHCvuvT/Hiv1Wv/ZegLPf3kAF/3+S3y66zQqaltgc7ggpVQzkm95FZeHc9mo3gHfz9uzn38NALjUMza3nvF3oDOxzxQRURAWuxPDHvoIAJBpNqAgIwVFmZH1B/KnZKbSghSvTx6Yj6X3no+KWnc26s21RzHHMzUYLADT6QRG9Mnymc4Ktcff5X9eAQAY0isTfXNS8dbao3hh2QH8ZMYg9ZoNh2vw7QWrseSeae1WkiUT/9YDvbPNuH5Sf1w1oR/ONFjbdTbvDMr+fWeX5fkc9y9+P1DZjL98uR+bPB3GBxSm42Cle0VnNKvrlG76Dpdve4RbXluHg1XN+Ostk/D0J/sAAI9dORqo4obZ8cLMFBFREP/27KsGAI0WB0b3yw5xdWg2JTMV5kM81atb+q8+dDdrDNSLSvE9T7sEALjjgoFotjoCtkrwzlb8ZMZA3D9rGADgpNcGudVNVnx7wWoA7q7byezNdUfVx1dN6Kc+TjHou2z5/5jibHz28/Pxw+m+/awGFrZfkacEUgDUQMpk0KmbKEdC2QLG4Wz7s7Y7XfhibyWOVLfggqe/BAAM6ZWBwkyvgI41UzFjMEVEFMQhv35PPh9AUQo3zacIlDFJDRGAzRzVGxP65+DP149HeooBLunupeRPmTL85ezhyEkzIdNsRP+8NJ/tbOYvaavfCfWeyUDJ8JkMOjz97bEJGYMQAgMLM9oKvT0G92pbSfjfu6biv3dN9Tk/zLPdUJbZAJ0u8uImg2cq2Dtwrmpqv8qwrSCehVPxwmk+IqIgTtZbUJqfhppmGxosDhTFFEy5P+D82yD4CxRMheqJJITAez+ZAgB4ffVhAMDS3adxusGCG87pjzSTAcu/rsRJT0FzplfD0YwUA5o8TR6llPjnxrZ+TNVeRcxNVgeqm6w4Kz8dTVYHthytQ266ESP7djxT19kGFroL+d++/dyoApKusvx/L4DZqFcD9KsnFuOfGyvw6JWjcOOk/pj33jZcU14S1T2VzJTd5cLne05jwVcHA67sO39Iod8RZqZixWCKiCiI0w0WFGWZcbjaneVQPqA7QslMBdtYV6HXCXzys+l4d2MFXlh2EPOvGh1yms/bIM9KwLsWbgYA9M1JxU/e3ORzjfd2IHnpJpxqsGD515V4c417WuyCoYU4UNnssyLsxpfXqj2TFFlmA7Y9chkAd33SnlONGNFXOzVWrZ6tXc7SaEdv/6lGZa++ykYrdDqB33Ugm6bUTDldEvPe3Y4zXr2v5s0ahvlL9uCnFw/GnRd4auS4pC9uGEwREQVR2WjFSK8AoSCGzJRSgG4KE0wB7gLx+2cPx/2zh0f1HpMH+HZGV/ag89Y7u62A3mzUYcf+BnznlXXqsT9eOw7f/+t6HK5ugcslcbCqqV0gBQANFgeueG4l6lpsONNgRavdiTsuGIhfXDYsqjF3lhZPMNWRNhaJ8L0ppdhaUYebzunf4XsoPcTsTomygnQ1mPrZxUPwo/MH4rZpA9SAi+KLNVNEREEcqmpGYWYKzEb3j8qMlI7XESmbExdlxW8bE3/+tTl/+uxrn/cGgDKv7UjO9Qu+ACAnzYShvbOw9VgdBjywGBf/wb09ypRB+fjrLWf7vH7rsTocqW5R93p77osDWLW/qt09E6GqyQqzUaf+2WldQUYK/n7rOSjK6thqUQDQ691//o/+d5fPCs9rzm7b0DggFqDHLDlCdiKiLmJ1ODH0wY/ULE+aSY8Ugx4WuyvqPfm8/eySIbhlSmnYLtax2vjgxdAJgfGPfqoeW3DTBAz65RKcU5bns8ff0N5thdDfnXwWHrrcveHt3LF98c8Nx/DdyaXIzzChMCMFV5cXQwiB3tlmNFkcKC/Ng9Ml8eKygxhXkoOcNCNm/Wk5Dle34Ly2TgsJ8/WZpoDF392ZIUiw1DtogNZzvjedjcEUEZGX1Qeq3f8/6P7/rFF9YLW78PKKQz71RtHS68JvBxIPyns8e8N43PnWZpiNOhj0Oqy+/0Lkp/u+/9RBBXjsylEozU/36bI9eWA+vv7trICBiPdWOHqdwI9nDAQAdVVgSwQb7Xa27RX1WLavEt8c3y/8xd1IsGAqfEDJzFSsGEwREXlcs2A11h32Xf1UkpuG+2cPxx0XDEJGDJmprnb5mL44pyxfndrpk92+X5EQAjf5bVPjfS4aSm2Ssjowkb7xrLs56SU+Xb67P71OwGzUqa0xXrvlbGSG+jvbg7J2nS15fjIQEXUy/33tLh7eC9lp7mxUbnp8N8btCrH0xYqWErT939KvMXlAPs4JUI/V2Wb+3zJM88qweW823BMIITCqbzY2HKlFv5xUXDA0wq+fNVMxYzBFRORx7oB8rNhfhR+ePwCDCjN63DRRrL4xti/+s/UEtlbUdVkw9e7GChytacGAwnTsOdWIPZ4VjI9eOapLtozRGmUF3/G61giuZmYqXhhMERF5tNgcmDIoH/fPiq4lAbk9+a3R+M/WE3C2b8DeKd5YcwQPBtnY+dIeNsWnyM8w4WhNCyaeFc1GzsxMxSo51owSEcXB6QaLz/Yp/lrtLqQa+TtmR6Ua9dDrBJqswb/H8SKlbBdIvfTdcggBPDhnuNoEs6e5ypNN9V6pSZ2PPzWIqEewOpw45/HPUH5WLv714/PanT9R14rdJxui2liWfAkh3FvUWDq/CL2+tS1g++Rn05FqdG9gfOiJOZ3+3lp25fh+WLm/GndfODj8xSxAjxtmpoioR/j76iMAgA1HanHnW5vanf9szxkAwAXD/Pcto2ikm/Q43dB+c914q2txB1O//eYoDOmV2W57lp4q02zEgu9M9Ol0HxYL0GPGYIqIuq0Xlx1A6bxFWHWgCo8t2q0e/++2k7A6nOrz+Uv24KH3dyA3zYgbJnV8Ow8CTtRb8NHOUz6Zo86gtGAoyuyZ03nxwcxUvDCYIqJu6/HFewAAN7y0tt25n769BQCwcN1RLPjqAADgouG9elTH7M409tef4P73tsd0D6dLwukKnDVp9EwlpsewxQ8pmJmKFYMpIuoRUgw6vPPDyerzNQersfpAtc8H/l0XamAflCT3p+vGqY8XrjuKw1XNAIA/fLoPpfMW4aMdJ1Hfasf8JXuw1tNlPphL/vAVLnj6S/X56QYL5i/Zg5tfXYfrX1oDACjqwl5a3Q5/cYgbFqATUY9w4bAiTCrLw6EnZuPOhZuxaNtJ9QMZAA49MZtZqTiYM7oP7vFk/QDg95/uw5+vH49nPJsu/+iNtnq1RdtPYPn/XhjwPi98dQAHPYHYD/62Hkt3n2l3zf9cOgSDirhqLWZMTMWMwRQRdUtKTc19M4dhSK8MlJfmAXCvOCvLT/e59rXvnc1AKk4Met8Jj72nGoJee6ymFaXzFmHOmD741eUjUJRlxpkGC5Z9XYVXVhxSr1MCqYIME+65aDDGleTiTKMFFw3vmb2k4od/5+OFwRQRdUtPLHYXnBfnprb70HX5rV6aMZQr+OLphnP644s9ZzBjaBEWrjuK77zirlnrk23GyXoLBhVl4LqzS9RFAYu2ncSibScx8axcbDzStqXPD88fgLlj++KLPWdwdmmeX1f17K78kro5pqZixWCKiLodq8OJN9ceBQCM6JvV7rxSZ/PYlaNww6T+zErF2ePfHA3AvdXLwnVHsfzrKgDAP26fDINeoK+nl9fJegucLoljNS34fO8ZNZAa1jsT82YNw5RBBTDqdRjZl4ETaRuDKSJKGjaHCyZD+HUzS7afAgDcMqUUAwsz2p3/zuRSZKcZccXYftDpGEh1livH94NBNPnzXgAAE9xJREFUL3DP21tgMujQLzdV3RAZAB66fIT6uKrJilP1Fmw8UosLhhahfz77RnU6/hIRNwymiEjzGix23PrX9Vh/uBZf/M8MlBWkB70uM8WAN9YcgUmvw0NzRgS8Tq8T+Ob44s4cMsH9fb5iXD+MK8lBTbPNJ5DyV5CRgoKMFIzqxyxUl2PTzpgxmCIizXti8R6sP+yeAlKWyr9yczmmDymEgLvoefPRWnzz+VU+r2PWSRvOyk/HWfmBA2BKJP77iBcGU0SkWb//ZC9G9s3CrhP17c7d+rcN6uNfXDYULTbf/eAemD2s08dH1D0wMxUrBlNEpEkOpwt//ny/+vzWqWVYd6gG24/XY0BhOg5WNqvnnvp4r/p4RJ8s/O37k1DIZo5EoTExFTcMpohIk1b7dcf+xWVDUd1sw/pDNbhyfD9UN1nx1tqjsDiceO4L93YwxbmpWHzPtEQMlyh5sWYqZgymiEiT/rbqsPq4rCAdZqMe/XJS0W98PwBAfkYK7rpoMKSUajB1xwXcDoaIuh6DKSLSlAaLHS98dQBLd5/B3RcOwr2XDg15vRACb99+Ln7+zlZMHVTQRaMk6g6UeT5mpmLFYIqINMNid2LMI5+oz28896yIXnfugHysnBd4jzcios4WvvsdEVEXOF7XivOf+kJ9ftu0MvTKMidwRETdHJt2xg0zU0QUd602JzYeqcXC9UdRkpuG3lkpmD6kEAM83cillKiobcXJegtON1iw9lA13lhzFCaDDn+6bhyuGNcvwV8BUQ/CAvSYJUUw9ftP9uLPn+/H9ZNK8MRVYxI9HKIeye504Ud/34g5Y/rgqgm+3cP3n2nEZ7vPoLQgHYermvHEkj0B7zH/qtE4XteKt9cfQ2Wj1edcr6wUzP/WGFwwtKjTvgYi8sbMVLxoPphqsjrUXjML1x3DwnXHfH5z3Xe6ETe9vBZ9c1JxyYheSDXq4ZISxblp6JNtxpjibG5iShSDjUdqcffCzahrsaHZ5sRne87gTKMVNc02HKluRqPFgVUHfNsYZKca8a0Jxbj5vLPw039sQYpBhzUHazDvve0AgIGF6bj7osHISNEjJ82EaYMKYNCz6oAoMZiZipWmg6lVB6pww0trAQAT+udg09E6AMA9b2/B8q+rsPloLY7VtsLmcOFMoxVbjtW1u8egogz8cs5wjC/JQZbZiON1rahqsqJXlhkFGSkRbZoaSGWjFXtONaBPthml+enqB0FNsw11LTZkmo0w6gVSDHqYjToGdBSSyyXxf0v34V8bK1Bemoepgwtg0utQmJkCKQGb0wm9Toc0kx5DijKRnWYMeB8pJawOF8xGvfrc6ZJwSgmLzYX6VjscLhd2nWzA+kM1aLE5kWbSw6jXYX9lE0x6HawOFypqWyCEQH2rXc0gDe2Vib2nGwEA8z2Zp7x0E9JT9LhqfD9MKssDAAzpnYmxxTnqPmz//skU2J0ufLLzNP69uQKl+em4f/bwkPu0EVEX4OdS3AgZwVypEGImgD8B0AN4WUo53+98CoDXAUwEUA3gWinl4VD3LC8vlxs2bAh6XkqJsvsXAwDmjOmDX31jBKbO/wKzR/fG+1tOqNdN6J+Duy8ajAEFGTjTaIFBr0N9qx0A8MGW43hv0/EQXxdQmp+Okrw0fHtiMSYPyEdGigFvrj2CJqsD5w8pxLDeWbA5XHC4XHBKiWX7qvCP9Uex6WgdnK62712aSY8ssxGnGizt3ifLbEDvbDNabE64XBKlBekY1jsLJXmpSDHokZ9hQqpRD4NeoCgzBQMLMyIKvpwuyQ+kEKSUQb+PZxosSDHq1e9hRooByr8FnRBB93STUsLulNAJ9yau4f6c7E4Xjte2wiUlTAYdpHSXJwgBGPU6LNp+Eqv2V2FrRR2qmmwRf20FGSnISNHD7MnE6nU69b1a7U4YdAJOKSMuhTDpdeibY4ZRr4NBr0NxbioMOoEssxH5GSZcU16C0oJ0tNqcWLHf/YvMtycWqzVQRJSETm4FXpgOXPsmMPzyRI9G84QQG6WU5QHPhQumhBB6APsAXAKgAsB6ANdLKXd5XfMTAGOklD8SQlwH4JtSymtD3TdUMPXxzlOYv2QPDlW5t4s4+Phs6HQCTpf7Q+wHf9uAPaca8dotZ2NIr8yg7yGlxOoD1Xjm86+x5mANAGBMcTYGFWVgVN9sNFjs2HS0Dsv2VYb8HgRy4bAiTB1UAL1OYN3hGqR5PpizUo0YWJQBm8MFp8sFu1PiVL0FR2ta8P/t3XuMXOdZx/HvM9ed2fv6Fsfr2A513KaVcyElBJqQ4La0pWojlEqhJQSpVRA0qAEiSAG1BIGgCJVSFdpaSQEVmqqXQEMpSVqCoCJpiJParhM3F8e5+La+rne9l7k+/PGetTaO7dlkzsyZHf8+0mjPnDn77vv+fDz77HvemenryVCvO999aoxq/cy5F7JpVg72MDpSpD+foVwLM299+TQXjPQyOlxgqlTla1teZrpc49oNy8ikUhybLpNLp1g9UmRZf57hYo7hYpYlfXn68hmGilnOHyoAMDYxy0hvjmw0ozZXVLg7U+UaYxOzHJsq48CakSK9+Qy5TIrMAgqIRqq1OlOlGoenSuwbn6G/J0sunSKXMXLpNNmMkUunKOYyFHLpBbVZrtY5MlXi8GSZg5Oz/Pv2/dz7w728de0w16xfRqlaZ3K2wotHp9k3PsMzYyfO2FY2bSzv72GomGWomGW6XGNytsrkbIXx6QrlWh2AoUIWM6Naq5NKGUaYLK/WnGq9Hn1tXM2cNxAuR28cHeRDV67hr7/3DOuX97FmSS+ZtJEyoyebplKrM1Wq8vXH91Cq1Cnm0sxUahhQd0inYHS4yGAhS6laC0WhGelUuGXTxkhvnlq9zurhIlesHSGbDv+W4ftVmIucU1RMvSbNFlNXAX/s7r8Q3f84gLv/+bxjHoiOecTMMsABYJmfpfE3XLzR/2DztxifqXB8usz+47OkzNh3fIbte8KHmn70up/gtrdfdPIXfrPK1fppL+udKFX56v+9xEy5xkNPH2TjqkGuv2wVz46d4PBUiXwmHX0yvbFysMCmNy5v6tPoj09XeHjXYd6yahB3GJucxYByrc7uw1M8susIJ0pVdu6fYGyiFN71ebhAuVrnxSNTHJuukI6Ky4GeDLlMmr58msFijlKlxt7xGSZnq6f92b25NCsGe3j+0BSFbJr+nlCsjU9XyKaNuvOKGbdTpQxymRRDhdzJ4qvu4IRZEGfuhSHhfjo112YoLMu1OuVqfcFZLenNYWZUanVK1RqZVOrkv2GlVqdSW1jRkjJC0ZZJsWqoQG8+zRVrRhguZhmPZjLTZjjwo73HOThZophNU6rWKOYy9PeE22AhSyGbxoHDJ8qkU5BJpajPO9UzqRSZtJFJGblMivMGeqi5nyxEU2bU6nXKNWfjqkEuWT204DxERGKzfzt88Wq47o9gzVVJ96bj2bqrmyqmbgDe5e4fie7fBFzp7rfOO2ZHdMye6P6u6JjDZ2o3v3K9r7z5M5jBQE+WvnyGfCbFioEe3nz+AB+88oJz/hJCtVbnRKnKUDH3iv3T5So9mfRZC7rjMxUmZipMzlY5MlVifLrCy8emuX/HAZb35xkdLmIWXsKey6Q4MVtlaX8+XNopZDlvIMzMzFbC+pla3SlX65Sqoag5OhUKkJSFS1aGha8GYMx1rVYPl9rmCotsOkVvLk1vPkNfT4aVgz1Ua2GdT7lWpxJ9LVfr7BufYWwiFNkDhVAIVaLHzDhZWGVSYeZmSV+OpX15lvblWTEQxnhoskQunWKgkNG6NRGR+Y4+D5+9LOleLBp258QZi6m2LkA3s1uAWwBWrV7D1k+8g/6erC4vnEEmnXpVIQVQzDX+ZxssZBksvHqR8m9ee259dtmy/nzSXRAR6UwjF8Kvfx9mX/3iLTmNO3/ujA8tpJjaC6yed3802ne6Y/ZEl/kGCQvRX8HdNwObIayZOl2hICIiIm2yUu/dGIeFLEZ6DFhvZuvMLAfcCNx3yjH3ATdH2zcAD51tvZSIiIhIt2g4M+XuVTO7FXiA8NYIX3L3J83sT4At7n4fcDfwZTN7DjhKKLhEREREut6C1ky5+3eA75yy7xPztmeBD8TbNREREZHOp89vEBEREWmCiikRERGRJqiYEhEREWmCiikRERGRJqiYEhEREWmCiikRERGRJqiYEhEREWmCiikRERGRJqiYEhEREWmCiikRERGRJqiYEhEREWmCiikRERGRJpi7J/ODzSaBp2NqbilwOIZ2BoHjXdgOKKOF6NaM4mxLGTWmjBpTRo3FlRF0Xk6L9Xl7g7v3n/YRd0/kBmzptLaAzd3YjjI6tzOKuU/KSBkpo0WUUSfmtFift8/2s3SZ75X+rUvbiVOnjU0Ztb+tOCijxpRRY8poYTotp67LKMnLfFvc/YpOa6tbKaPGlFFjyqgxZdSYMmpMGS1MO3M6289KcmZqc4e21a2UUWPKqDFl1JgyakwZNaaMFqadOZ3xZyU2MyUiIiLSDbRmSkRERKQJKqZEREREmtCRxZSZrTaz/zKzp8zsSTP7WLR/xMy+a2bPRl+Ho/1vNLNHzKxkZrefpr20mf3QzL7d7rG0SpwZmdkLZvYjM9tqZluSGE8rxJzRkJl9w8x+bGY7zeyqJMYUt7gyMrMN0fkzd5sws9uSGlecYj6PfjtqY4eZ3WNmPUmMKW4xZ/SxKJ8nu+UcgteV0YfMbHv03PywmV0yr613mdnTZvacmd2R1JhaIeacvmRmB81sR8s7Htd7PcR5A1YCl0fb/cAzwMXAXwJ3RPvvAD4VbS8H3gr8GXD7adr7HeArwLeTHlsnZgS8ACxNekwdntE/Ah+JtnPAUNLj67SM5rWZBg4Aa5IeXydlBKwCdgOF6P7XgF9LenwdltFbgB1AEcgA3wPekPT4EsroZ4DhaPvdwKPRdhrYBVwYPRdtAy5OenydllN0/xrgcmBHq/vdkTNT7r7f3Z+ItieBnYQnovcTfqkRfb0+Ouaguz8GVE5ty8xGgV8E7mpD19smzoy6VVwZmdkg4T/l3dFxZXcfb8sgWqxF59EmYJe7v9iyjrdRzBllgIKZZQgFw74Wd78tYszoTYRfhtPuXgX+G/ilNgyh5V5HRg+7+7Fo/w+A0Wj7p4Dn3P15dy8DX43a6Aox5oS7/w9wtB397shiaj4zWwtcBjwKrHD3/dFDB4AVC2jiM8DvAfVW9K8TxJCRAw+a2eNmdktLOpmwJjNaBxwC/t7C5eK7zKy3VX1NSgzn0ZwbgXti7VyHaCYjd98L/BXwErAfOO7uD7asswlp8jzaAVxtZkvMrAi8B1jdoq4m5nVk9GHgP6LtVcDL8x7bE+3rOk3m1FYdXUyZWR/wTeA2d5+Y/5iHObyzvq+Dmb0XOOjuj7eul8lqNqPI29z9csIU6UfN7Jr4e5qcGDLKEKaKP+/ulwFThGnmrhHTeYSZ5YD3AV+PvZMJi+H5aJjw1/U64Hyg18x+pUXdTUSzGbn7TuBTwIPA/cBWoNaa3ibjtWZkZtcRioTfb1snO8Biy6ljiykzyxKC/Gd3vzfaPWZmK6PHVwIHGzTzs8D7zOwFwlToz5vZP7Woy20XU0ZzfzHj7geBfyFMI3eFmDLaA+xx90ej+98gFFddIa7zKPJu4Al3H4u/p8mJKaO3A7vd/ZC7V4B7Ces9ukKMz0d3u/tPuvs1wDHCmpmu8FozMrONhCUq73f3I9Huvbxytm402tc1YsqprTqymDIzI6xP2enun5730H3AzdH2zcC3ztaOu3/c3UfdfS3h0sND7t4VfwnGlZGZ9ZpZ/9w28E7CVPuiF+N5dAB42cw2RLs2AU/F3N1ExJXRPL9Ml13iizGjl4CfNrNi1OYmwnqQRS/O88jMlkdfLyCsl/pKvL1NxmvNKBr/vcBN7j6/oHwMWG9m66KZ4BujNrpCjDm119lWpyd1A95GmMLbTpjm3Uq4dr4E+E/gWcKrPEai488jzB5MAOPR9sApbV5Ld72aL5aMCK8I2RbdngT+MOmxdVpG0WOXAluitv6V6NUji/0Wc0a9wBFgMOlxdXBGdwI/JvzB8mUgn/T4OjCj7xP+WNkGbEp6bAlmdBdhZm7u2C3z2noPYcZuVzc9Z7cgp3sI6xMr0Tn24Vb1Wx8nIyIiItKEjrzMJyIiIrJYqJgSERERaYKKKREREZEmqJgSERERaYKKKREREZEmqJgSkY5nZjUz2xp9ivw2M/tdMzvr85eZrTWzD7arjyJy7lIxJSKLwYy7X+rubwbeQXin9U82+J61gIopEWk5vc+UiHQ8Mzvh7n3z7l9IeCfopcAawhtgzn349K3u/rCZ/QB4E7Cb8CnznwX+gvAGvnngb939i20bhIh0LRVTItLxTi2mon3jwAZgEqi7+6yZrQfucfcrzOxa4HZ3f290/C3Acnf/UzPLA/8LfMDdd7d1MCLSdTJJd0BEpElZ4HNmdilQAy46w3HvBDaa2Q3R/UFgPWHmSkTkdVMxJSKLTnSZr0b45PhPAmPAJYR1oLNn+jbgt9z9gbZ0UkTOGVqALiKLipktA74AfM7DOoVBYL+714GbgHR06CTQP+9bHwB+w8yyUTsXmVkvIiJN0syUiCwGBTPbSrikVyUsOP909NjfAd80s18F7gemov3bgZqZbQP+Afgbwiv8njAzAw4B17drACLSvbQAXURERKQJuswnIiIi0gQVUyIiIiJNUDElIiIi0gQVUyIiIiJNUDElIiIi0gQVUyIiIiJNUDElIiIi0oT/BzLBLdE8e1BFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-lSjFE-4_qq"
      },
      "source": [
        "When we scale the block reward and the Bitcoin price, we can see the price goes up as the block reward goes down, perhaps this information will be helpful to our model's performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7m0h2WcP5Ko8"
      },
      "source": [
        "## Making a windowed dataset with pandas\n",
        "\n",
        "Previously, we used some custom made functions to window our **univariate** time series.\n",
        "\n",
        "However, since we've just added another variable to our dataset, these functions won't work.\n",
        "\n",
        "Not to worry though. Since our data is in a pandas DataFrame, we can leverage the [`pandas.DataFrame.shift()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.shift.html) method to create a windowed **multivariate** time series.\n",
        "\n",
        "The `shift()` method offsets an index by a specified number of periods.\n",
        "\n",
        "Let's see it in action."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_9iFxKS8Sed"
      },
      "source": [
        "# Setup dataset hyperparameters\n",
        "HORIZON = 1\n",
        "WINDOW_SIZE = 7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "DqPp3HdHsXO8",
        "outputId": "82838ef8-9ee5-4967-bfd0-25a5e8a72eef"
      },
      "source": [
        "# Make a copy of the Bitcoin historical data with block reward feature\n",
        "bitcoin_prices_windowed = bitcoin_prices_block.copy()\n",
        "\n",
        "# Add windowed columns\n",
        "for i in range(WINDOW_SIZE): # Shift values for each step in WINDOW_SIZE\n",
        "  bitcoin_prices_windowed[f\"Price+{i+1}\"] = bitcoin_prices_windowed[\"Price\"].shift(periods=i+1)\n",
        "bitcoin_prices_windowed.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>block_reward</th>\n",
              "      <th>Price+1</th>\n",
              "      <th>Price+2</th>\n",
              "      <th>Price+3</th>\n",
              "      <th>Price+4</th>\n",
              "      <th>Price+5</th>\n",
              "      <th>Price+6</th>\n",
              "      <th>Price+7</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>123.65499</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>125.45500</td>\n",
              "      <td>25</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>108.58483</td>\n",
              "      <td>25</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>118.67466</td>\n",
              "      <td>25</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>121.33866</td>\n",
              "      <td>25</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-06</th>\n",
              "      <td>120.65533</td>\n",
              "      <td>25</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-07</th>\n",
              "      <td>121.79500</td>\n",
              "      <td>25</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-08</th>\n",
              "      <td>123.03300</td>\n",
              "      <td>25</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-09</th>\n",
              "      <td>124.04900</td>\n",
              "      <td>25</td>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-10</th>\n",
              "      <td>125.96116</td>\n",
              "      <td>25</td>\n",
              "      <td>124.04900</td>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Price block_reward    Price+1  ...    Price+5    Price+6    Price+7\n",
              "Date                                           ...                                 \n",
              "2013-10-01  123.65499           25        NaN  ...        NaN        NaN        NaN\n",
              "2013-10-02  125.45500           25  123.65499  ...        NaN        NaN        NaN\n",
              "2013-10-03  108.58483           25  125.45500  ...        NaN        NaN        NaN\n",
              "2013-10-04  118.67466           25  108.58483  ...        NaN        NaN        NaN\n",
              "2013-10-05  121.33866           25  118.67466  ...        NaN        NaN        NaN\n",
              "2013-10-06  120.65533           25  121.33866  ...  123.65499        NaN        NaN\n",
              "2013-10-07  121.79500           25  120.65533  ...  125.45500  123.65499        NaN\n",
              "2013-10-08  123.03300           25  121.79500  ...  108.58483  125.45500  123.65499\n",
              "2013-10-09  124.04900           25  123.03300  ...  118.67466  108.58483  125.45500\n",
              "2013-10-10  125.96116           25  124.04900  ...  121.33866  118.67466  108.58483\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVN20lqn6b_Y"
      },
      "source": [
        "Now that we've got a windowed dataset, let's separate features (`X`) from labels (`y`).\n",
        "\n",
        "Remember in our windowed dataset, we're trying to use the previous `WINDOW_SIZE` steps to predict `HORIZON` steps.\n",
        "\n",
        "```\n",
        "Window for a week (7) to predict a horizon of 1 (multivariate time series)\n",
        "WINDOW_SIZE & block_reward -> HORIZON\n",
        "\n",
        "[0, 1, 2, 3, 4, 5, 6, block_reward] -> [7]\n",
        "[1, 2, 3, 4, 5, 6, 7, block_reward] -> [8]\n",
        "[2, 3, 4, 5, 6, 7, 8, block_reward] -> [9]\n",
        "```\n",
        "\n",
        "We'll also remove the `NaN` values using pandas [`dropna()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html) method, this equivalent to starting our windowing function at `sample 0 (the first sample) + WINDOW_SIZE`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "VpMH6_Dax2wJ",
        "outputId": "a03fbc0d-3bd4-499a-c1f0-7305450ae580"
      },
      "source": [
        "# Let's create X & y, remove the NaN's and convert to float32 to prevent TensorFlow errors \n",
        "X = bitcoin_prices_windowed.dropna().drop(\"Price\", axis=1).astype(np.float32) \n",
        "y = bitcoin_prices_windowed.dropna()[\"Price\"].astype(np.float32)\n",
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>block_reward</th>\n",
              "      <th>Price+1</th>\n",
              "      <th>Price+2</th>\n",
              "      <th>Price+3</th>\n",
              "      <th>Price+4</th>\n",
              "      <th>Price+5</th>\n",
              "      <th>Price+6</th>\n",
              "      <th>Price+7</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-08</th>\n",
              "      <td>25.0</td>\n",
              "      <td>121.794998</td>\n",
              "      <td>120.655327</td>\n",
              "      <td>121.338661</td>\n",
              "      <td>118.674660</td>\n",
              "      <td>108.584831</td>\n",
              "      <td>125.455002</td>\n",
              "      <td>123.654991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-09</th>\n",
              "      <td>25.0</td>\n",
              "      <td>123.032997</td>\n",
              "      <td>121.794998</td>\n",
              "      <td>120.655327</td>\n",
              "      <td>121.338661</td>\n",
              "      <td>118.674660</td>\n",
              "      <td>108.584831</td>\n",
              "      <td>125.455002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-10</th>\n",
              "      <td>25.0</td>\n",
              "      <td>124.049004</td>\n",
              "      <td>123.032997</td>\n",
              "      <td>121.794998</td>\n",
              "      <td>120.655327</td>\n",
              "      <td>121.338661</td>\n",
              "      <td>118.674660</td>\n",
              "      <td>108.584831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-11</th>\n",
              "      <td>25.0</td>\n",
              "      <td>125.961159</td>\n",
              "      <td>124.049004</td>\n",
              "      <td>123.032997</td>\n",
              "      <td>121.794998</td>\n",
              "      <td>120.655327</td>\n",
              "      <td>121.338661</td>\n",
              "      <td>118.674660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-12</th>\n",
              "      <td>25.0</td>\n",
              "      <td>125.279663</td>\n",
              "      <td>125.961159</td>\n",
              "      <td>124.049004</td>\n",
              "      <td>123.032997</td>\n",
              "      <td>121.794998</td>\n",
              "      <td>120.655327</td>\n",
              "      <td>121.338661</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            block_reward     Price+1  ...     Price+6     Price+7\n",
              "Date                                  ...                        \n",
              "2013-10-08          25.0  121.794998  ...  125.455002  123.654991\n",
              "2013-10-09          25.0  123.032997  ...  108.584831  125.455002\n",
              "2013-10-10          25.0  124.049004  ...  118.674660  108.584831\n",
              "2013-10-11          25.0  125.961159  ...  121.338661  118.674660\n",
              "2013-10-12          25.0  125.279663  ...  120.655327  121.338661\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5k_UmcPqzTmm",
        "outputId": "67ecadc5-dff4-404a-efe6-e02a6f7c6288"
      },
      "source": [
        "# View labels\n",
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2013-10-08    123.032997\n",
              "2013-10-09    124.049004\n",
              "2013-10-10    125.961159\n",
              "2013-10-11    125.279663\n",
              "2013-10-12    125.927498\n",
              "Name: Price, dtype: float32"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECzmudIK8BW5"
      },
      "source": [
        "What a good looking dataset, let's split it into train and test sets using an 80/20 split just as we've done before."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GtEFj6HsVwy",
        "outputId": "bd1458e0-814f-4150-938c-7931cd6c1bee"
      },
      "source": [
        "# Make train and test sets\n",
        "split_size = int(len(X) * 0.8)\n",
        "X_train, y_train = X[:split_size], y[:split_size]\n",
        "X_test, y_test = X[split_size:], y[split_size:]\n",
        "len(X_train), len(y_train), len(X_test), len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2224, 2224, 556, 556)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pbfw5uqEzlZ3"
      },
      "source": [
        "Training and test multivariate time series datasets made! Time to build a model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "APXrtOva8cNH"
      },
      "source": [
        "## Model 6: Dense (multivariate time series)\n",
        "\n",
        "To keep things simple, let's the `model_1` architecture and use it to train and make predictions on our multivariate time series data.\n",
        "\n",
        "By replicating the `model_1` architecture we'll be able to see whether or not adding the block reward feature improves or detracts from model performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AppTkPafzVyg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30916a1e-ded9-42d5-b295-6e7e72da7346"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# Make multivariate time series model\n",
        "model_6 = tf.keras.Sequential([\n",
        "  layers.Dense(128, activation=\"relu\"),\n",
        "  # layers.Dense(128, activation=\"relu\"), # adding an extra layer here should lead to beating the naive model\n",
        "  layers.Dense(HORIZON)\n",
        "], name=\"model_6_dense_multivariate\")\n",
        "\n",
        "# Compile\n",
        "model_6.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "\n",
        "# Fit\n",
        "model_6.fit(X_train, y_train,\n",
        "            epochs=100,\n",
        "            batch_size=128,\n",
        "            verbose=0, # only print 1 line per epoch\n",
        "            validation_data=(X_test, y_test),\n",
        "            callbacks=[create_model_checkpoint(model_name=model_6.name)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
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            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
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            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
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            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n",
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            "INFO:tensorflow:Assets written to: model_experiments/model_6_dense_multivariate/assets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdceed05590>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCN3aJiZ1CKf"
      },
      "source": [
        "Multivariate model fit!\n",
        "\n",
        "You might've noticed that the model inferred the input shape of our data automatically (the data now has an extra feature). Often this will be the case, however, if you're running into shape issues, you can always explicitly define the input shape using `input_shape` parameter of the first layer in a model.\n",
        "\n",
        "Time to evaluate our multivariate model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1miRk_b3g97",
        "outputId": "eb0b3763-61c3-4e8a-d793-10a6eb8c9afb"
      },
      "source": [
        "# Make sure best model is loaded and evaluate\n",
        "model_6 = tf.keras.models.load_model(\"model_experiments/model_6_dense_multivariate\")\n",
        "model_6.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 567.5873\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "567.5873413085938"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1t9d_Yyx17dE",
        "outputId": "156d4f26-4718-4097-ded0-01ba3bc6974a"
      },
      "source": [
        "# Make predictions on multivariate data\n",
        "model_6_preds = tf.squeeze(model_6.predict(X_test))\n",
        "model_6_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([8836.276, 8763.8  , 9040.486, 8741.225, 8719.326, 8765.071,\n",
              "       8661.102, 8496.891, 8463.231, 8521.585], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsMPgRO117aB",
        "outputId": "b1e75c26-fe15-4006-9621-82a18ff4dc67"
      },
      "source": [
        "# Evaluate preds\n",
        "model_6_results = evaluate_preds(y_true=y_test,\n",
        "                                 y_pred=model_6_preds)\n",
        "model_6_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 567.5874,\n",
              " 'mape': 2.541387,\n",
              " 'mase': 0.99709386,\n",
              " 'mse': 1161688.4,\n",
              " 'rmse': 1077.8165}"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KY6-tZcK0jJF"
      },
      "source": [
        "Hmmm... how do these results compare to `model_1` (same window size and horizon but without the block reward feature)?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HwS5Onbh0ir7",
        "outputId": "b3226442-4a04-4bb4-f14a-b8813d4b56a7"
      },
      "source": [
        "model_1_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 568.95123,\n",
              " 'mape': 2.5448983,\n",
              " 'mase': 0.9994897,\n",
              " 'mse': 1171744.0,\n",
              " 'rmse': 1082.4713}"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdLrFEG60qtL"
      },
      "source": [
        "It looks like the adding in the block reward may have helped our model slightly.\n",
        "\n",
        "But there a few more things we could try.\n",
        "\n",
        "> 📖 **Resource:** For different ideas on how to improve a neural network model (from a model perspective), refer to the [*Improving a model*](https://dev.mrdbourke.com/tensorflow-deep-learning/02_neural_network_classification_in_tensorflow/#improving-a-model) section in notebook 02.\n",
        "\n",
        "\n",
        "> 🛠 **Exercise(s):** \n",
        "> 1. Try adding an extra `tf.keras.layers.Dense()` layer with 128 hidden units to `model_6`, how does this effect model performance?\n",
        "> 2. Is there a better way to create this model? As in, should the `block_reward` feature be bundled in with the Bitcoin historical price feature? Perhaps you could test whether building a multi-input model (e.g. one model input for Bitcoin price history and one model input for `block_reward`)  works better? See [*Model 4: Hybrid embedding*](https://dev.mrdbourke.com/tensorflow-deep-learning/09_SkimLit_nlp_milestone_project_2/#model-4-combining-pretrained-token-embeddings-character-embeddings-hybrid-embedding-layer) section of notebook 09 for an idea on how to create a multi-input model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPkzQccU-dtR"
      },
      "source": [
        "## Model 7: N-BEATS algorithm\n",
        "\n",
        "Time to step things up a notch.\n",
        "\n",
        "So far we've tried a bunch of smaller models, models with only a couple of layers.\n",
        "\n",
        "But one of the best ways to improve a model's performance is to increase the number of layers in it.\n",
        "\n",
        "That's exactly what the [N-BEATS (Neural Basis Expansion Analysis for Interpretable Time Series Forecasting) algorithm](https://arxiv.org/pdf/1905.10437.pdf) does.\n",
        "\n",
        "The N-BEATS algorithm focuses on univariate time series problems and achieved state-of-the-art performance in the winner of the [M4 competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128) (a forecasting competition).\n",
        "\n",
        "For our next modelling experiment we're going to be replicating the **generic architecture** of the N-BEATS algorithm (see [section 3.3 of the N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf)).\n",
        "\n",
        "We're not going to go through all of the details in the paper, instead we're going to focus on:\n",
        "1. Replicating the model architecture in [Figure 1 of the N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf) \n",
        "\n",
        "![figure 1 from N-BBEATS paper, the algorithm we're going to build](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-figure-1-nbeats-paper-annotated.png)\n",
        "*N-BEATS algorithm we're going to replicate with TensorFlow with window (input) and horizon (output) annotations.*\n",
        "\n",
        "2. Using the same hyperparameters as the paper which can be found in [Appendix D of the N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf)\n",
        "\n",
        "Doing this will give us an opportunity to practice:\n",
        "* Creating a custom layer for the `NBeatsBlock` by subclassing [`tf.keras.layers.Layer`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer)\n",
        "  * Creating a custom layer is helpful for when TensorFlow doesn't already have an existing implementation of a layer or if you'd like to make a layer configuration repeat a number of times (e.g. like a stack of N-BEATS blocks)\n",
        "* Implementing a custom architecture using the Functional API \n",
        "* Finding a paper related to our problem and seeing how it goes \n",
        "\n",
        "> 🔑 **Note:** As you'll see in the paper, the authors state “N-BEATS is implemented and trained in TensorFlow”, that's what we'll be doing too!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qukx3nigVsCg"
      },
      "source": [
        "### Building and testing an N-BEATS block layer\n",
        "\n",
        "Let's start by building an N-BEATS block layer, we'll write the code first and then discuss what's going on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llwhXCn9-hQt"
      },
      "source": [
        "# Create NBeatsBlock custom layer \n",
        "class NBeatsBlock(tf.keras.layers.Layer):\n",
        "  def __init__(self, # the constructor takes all the hyperparameters for the layer\n",
        "               input_size: int,\n",
        "               theta_size: int,\n",
        "               horizon: int,\n",
        "               n_neurons: int,\n",
        "               n_layers: int,\n",
        "               **kwargs): # the **kwargs argument takes care of all of the arguments for the parent class (input_shape, trainable, name)\n",
        "    super().__init__(**kwargs)\n",
        "    self.input_size = input_size\n",
        "    self.theta_size = theta_size\n",
        "    self.horizon = horizon\n",
        "    self.n_neurons = n_neurons\n",
        "    self.n_layers = n_layers\n",
        "\n",
        "    # Block contains stack of 4 fully connected layers each has ReLU activation\n",
        "    self.hidden = [tf.keras.layers.Dense(n_neurons, activation=\"relu\") for _ in range(n_layers)]\n",
        "    # Output of block is a theta layer with linear activation\n",
        "    self.theta_layer = tf.keras.layers.Dense(theta_size, activation=\"linear\", name=\"theta\")\n",
        "\n",
        "  def call(self, inputs): # the call method is what runs when the layer is called \n",
        "    x = inputs \n",
        "    for layer in self.hidden: # pass inputs through each hidden layer \n",
        "      x = layer(x)\n",
        "    theta = self.theta_layer(x) \n",
        "    # Output the backcast and forecast from theta\n",
        "    backcast, forecast = theta[:, :self.input_size], theta[:, -self.horizon:]\n",
        "    return backcast, forecast"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReXvsaxCKmOF"
      },
      "source": [
        "Setting up the `NBeatsBlock` custom layer we see: \n",
        "* The class inherits from `tf.keras.layers.Layer` (this gives it all of the methods assosciated with `tf.keras.layers.Layer`)\n",
        "* The constructor (`def __init__(...)`) takes all of the layer hyperparameters as well as the `**kwargs` argument\n",
        "  * The `**kwargs` argument takes care of all of the hyperparameters which aren't mentioned in the constructor such as, `input_shape`, `trainable` and `name`\n",
        "* In the constructor, the block architecture layers are created:\n",
        "  * The hidden layers are created as a stack of fully connected with `n_nuerons` hidden units layers with ReLU activation\n",
        "  * The theta layer uses `theta_size` hidden units as well as linear activation\n",
        "* The `call()` method is what is run when the layer is called:\n",
        "  * It first passes the inputs (the historical Bitcoin data) through each of the hidden layers (a stack of fully connected layers with ReLU activation)\n",
        "  * After the inputs have been through each of the fully connected layers, they get passed through the theta layer where the backcast (backwards predictions, shape: `input_size`) and forecast (forward predictions, shape: `horizon`) are returned\n",
        "\n",
        "![code mapping to image of N-BEATS basic block](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-nbeats-basic-block-replication-with-tensorflow-layer-subclassing.png)\n",
        "*Using TensorFlow layer subclassing to replicate the N-BEATS basic block. See section 3.1 of N-BEATS paper for details.*\n",
        "\n",
        "Let's see our block replica in action by together by creating a toy version of `NBeatsBlock`.\n",
        "\n",
        "> 📖  **Resource:** Much of the creation of the time series materials (the ones you're going through now), including replicating the N-BEATS algorithm were streamed live on Twitch. If you'd like to see replays of how the algorithm was replicated, check out the [*Time series research and TensorFlow course material creation playlist*](https://youtube.com/playlist?list=PL8IpyNZ21vUSCM7nRAuS-hW_E-sgwdmaI) on the Daniel Bourke arXiv YouTube channel."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6mdJ3pSOGwG"
      },
      "source": [
        "# Set up dummy NBeatsBlock layer to represent inputs and outputs\n",
        "dummy_nbeats_block_layer = NBeatsBlock(input_size=WINDOW_SIZE, \n",
        "                                       theta_size=WINDOW_SIZE+HORIZON, # backcast + forecast \n",
        "                                       horizon=HORIZON,\n",
        "                                       n_neurons=128,\n",
        "                                       n_layers=4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ne7XLO4gAGfm",
        "outputId": "820dbd38-c6ec-4344-84a9-ecb1fcaddfe6"
      },
      "source": [
        "# Create dummy inputs (have to be same size as input_size)\n",
        "dummy_inputs = tf.expand_dims(tf.range(WINDOW_SIZE) + 1, axis=0) # input shape to the model has to reflect Dense layer input requirements (ndim=2)\n",
        "dummy_inputs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(1, 7), dtype=int32, numpy=array([[1, 2, 3, 4, 5, 6, 7]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DtAT8Onk-hX_",
        "outputId": "37f17ed4-851e-434f-8ce0-916fdd2510e3"
      },
      "source": [
        "# Pass dummy inputs to dummy NBeatsBlock layer\n",
        "backcast, forecast = dummy_nbeats_block_layer(dummy_inputs)\n",
        "# These are the activation outputs of the theta layer (they'll be random due to no training of the model)\n",
        "print(f\"Backcast: {tf.squeeze(backcast.numpy())}\")\n",
        "print(f\"Forecast: {tf.squeeze(forecast.numpy())}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Backcast: [ 0.19014978  0.83798355 -0.32870018  0.25159916 -0.47540277 -0.77836645\n",
            " -0.5299447 ]\n",
            "Forecast: -0.7554212808609009\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRl9Vi32WFLM"
      },
      "source": [
        "### Preparing data for the N-BEATS algorithm using `tf.data`\n",
        "\n",
        "We've got the basic building block for the N-BEATS architecture ready to go.\n",
        "\n",
        "But before we use it to replicate the entire N-BEATS generic architecture, let's create some data.\n",
        "\n",
        "This time, because we're going to be using a larger model architecture, to ensure our model training runs as fast as possible, we'll setup our datasets using the `tf.data` API.\n",
        "\n",
        "And because the N-BEATS algorithm is focused on univariate time series, we'll start by making training and test windowed datasets of Bitcoin prices (just as we've done above).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2IMhORJV3gg"
      },
      "source": [
        "HORIZON = 1 # how far to predict forward\n",
        "WINDOW_SIZE = 7 # how far to lookback"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "WsUh9T2uvpA1",
        "outputId": "4ac3496f-dab0-4fbd-a128-1624fe938efc"
      },
      "source": [
        "# Create NBEATS data inputs (NBEATS works with univariate time series)\n",
        "bitcoin_prices.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>123.65499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>125.45500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>108.58483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>118.67466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>121.33866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Price\n",
              "Date                 \n",
              "2013-10-01  123.65499\n",
              "2013-10-02  125.45500\n",
              "2013-10-03  108.58483\n",
              "2013-10-04  118.67466\n",
              "2013-10-05  121.33866"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "iqAnqJvVvsgb",
        "outputId": "e5ca250c-6931-4354-c4cb-266c0525ca3d"
      },
      "source": [
        "# Add windowed columns\n",
        "bitcoin_prices_nbeats = bitcoin_prices.copy()\n",
        "for i in range(WINDOW_SIZE):\n",
        "  bitcoin_prices_nbeats[f\"Price+{i+1}\"] = bitcoin_prices_nbeats[\"Price\"].shift(periods=i+1)\n",
        "bitcoin_prices_nbeats.dropna().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Price+1</th>\n",
              "      <th>Price+2</th>\n",
              "      <th>Price+3</th>\n",
              "      <th>Price+4</th>\n",
              "      <th>Price+5</th>\n",
              "      <th>Price+6</th>\n",
              "      <th>Price+7</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-08</th>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-09</th>\n",
              "      <td>124.04900</td>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-10</th>\n",
              "      <td>125.96116</td>\n",
              "      <td>124.04900</td>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-11</th>\n",
              "      <td>125.27966</td>\n",
              "      <td>125.96116</td>\n",
              "      <td>124.04900</td>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "      <td>118.67466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-12</th>\n",
              "      <td>125.92750</td>\n",
              "      <td>125.27966</td>\n",
              "      <td>125.96116</td>\n",
              "      <td>124.04900</td>\n",
              "      <td>123.03300</td>\n",
              "      <td>121.79500</td>\n",
              "      <td>120.65533</td>\n",
              "      <td>121.33866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Price    Price+1    Price+2  ...    Price+5    Price+6    Price+7\n",
              "Date                                         ...                                 \n",
              "2013-10-08  123.03300  121.79500  120.65533  ...  108.58483  125.45500  123.65499\n",
              "2013-10-09  124.04900  123.03300  121.79500  ...  118.67466  108.58483  125.45500\n",
              "2013-10-10  125.96116  124.04900  123.03300  ...  121.33866  118.67466  108.58483\n",
              "2013-10-11  125.27966  125.96116  124.04900  ...  120.65533  121.33866  118.67466\n",
              "2013-10-12  125.92750  125.27966  125.96116  ...  121.79500  120.65533  121.33866\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tOweM-6wRBH",
        "outputId": "dc5d029d-a834-4b1b-fe45-e77f73624a18"
      },
      "source": [
        "# Make features and labels\n",
        "X = bitcoin_prices_nbeats.dropna().drop(\"Price\", axis=1)\n",
        "y = bitcoin_prices_nbeats.dropna()[\"Price\"]\n",
        "\n",
        "# Make train and test sets\n",
        "split_size = int(len(X) * 0.8)\n",
        "X_train, y_train = X[:split_size], y[:split_size]\n",
        "X_test, y_test = X[split_size:], y[split_size:]\n",
        "len(X_train), len(y_train), len(X_test), len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2224, 2224, 556, 556)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gihkBWc8YiIt"
      },
      "source": [
        "Train and test sets ready to go!\n",
        "\n",
        "Now let's convert them into TensorFlow `tf.data.Dataset`'s to ensure they run as fast as possible whilst training.\n",
        "\n",
        "We'll do this by:\n",
        "1. Turning the arrays in tensor Datasets using [`tf.data.Dataset.from_tensor_slices()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices)\n",
        "  * Note: `from_tensor_slices()` works best when your data fits in memory, for extremely large datasets, you'll want to look into using the [`TFRecord` format](https://www.tensorflow.org/tutorials/load_data/tfrecord)\n",
        "2. Combine the labels and features tensors into a Dataset using [`tf.data.Dataset.zip()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#zip)\n",
        "3. Batch and prefetch the Datasets using [`batch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch) and [`prefetch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch)\n",
        "  * Batching and prefetching ensures the loading time from CPU (preparing data) to GPU (computing on data) is as small as possible \n",
        "\n",
        "> 📖 **Resource:** For more on building highly performant TensorFlow data pipelines, I'd recommend reading through the [*Better performance with the tf.data API*](https://www.tensorflow.org/guide/data_performance) guide."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxCwT-b1v6cw",
        "outputId": "3daa2cbe-a80b-4ea0-d083-c68e8489b12c"
      },
      "source": [
        "# 1. Turn train and test arrays into tensor Datasets\n",
        "train_features_dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
        "train_labels_dataset = tf.data.Dataset.from_tensor_slices(y_train)\n",
        "\n",
        "test_features_dataset = tf.data.Dataset.from_tensor_slices(X_test)\n",
        "test_labels_dataset = tf.data.Dataset.from_tensor_slices(y_test)\n",
        "\n",
        "# 2. Combine features & labels\n",
        "train_dataset = tf.data.Dataset.zip((train_features_dataset, train_labels_dataset))\n",
        "test_dataset = tf.data.Dataset.zip((test_features_dataset, test_labels_dataset))\n",
        "\n",
        "# 3. Batch and prefetch for optimal performance\n",
        "BATCH_SIZE = 1024 # taken from Appendix D in N-BEATS paper\n",
        "train_dataset = train_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "train_dataset, test_dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<PrefetchDataset shapes: ((None, 7), (None,)), types: (tf.float64, tf.float64)>,\n",
              " <PrefetchDataset shapes: ((None, 7), (None,)), types: (tf.float64, tf.float64)>)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4niwahKFb03y"
      },
      "source": [
        "Data prepared! Notice the input shape for the features `(None, 7)`, the `None` leaves space for the batch size where as the `7` represents the `WINDOW_SIZE`.\n",
        "\n",
        "Time to get create the N-BEATS architecture. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQSC8sjxYM1N"
      },
      "source": [
        "### Setting up hyperparameters for N-BEATS algorithm\n",
        "\n",
        "Ho ho, would you look at that! Datasets ready, model building block ready, what'd you say we put things together?\n",
        "\n",
        "Good idea.\n",
        "\n",
        "Okay.\n",
        "\n",
        "Let's go.\n",
        "\n",
        "To begin, we'll create variables for each of the hyperparameters we'll be using for our N-BEATS replica.\n",
        "\n",
        "> 📖 **Resource:** The following hyperparameters are taken from Figure 1 and Table 18/Appendix D of the [N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf).\n",
        "\n",
        "![table 18 from N-BEATS paper](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-nbeats-table-18-hyperparameters.png)\n",
        "*Table 18 from [N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf) describing the hyperparameters used for the different variants of N-BEATS. We're using N-BEATS-G which stands for the generic version of N-BEATS.*\n",
        "\n",
        "> 🔑 **Note:** If you see variables in a machine learning example in all caps, such as \"`N_EPOCHS = 100`\", these variables are often hyperparameters which are used through the example. You'll usually see them instantiated towards the start of an experiment and then used throughout.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wK9Ix_zOesPO",
        "outputId": "4de1fd80-2884-4fad-baaa-8959cb36f308"
      },
      "source": [
        "# Values from N-BEATS paper Figure 1 and Table 18/Appendix D\n",
        "N_EPOCHS = 5000 # called \"Iterations\" in Table 18\n",
        "N_NEURONS = 512 # called \"Width\" in Table 18\n",
        "N_LAYERS = 4\n",
        "N_STACKS = 30\n",
        "\n",
        "INPUT_SIZE = WINDOW_SIZE * HORIZON # called \"Lookback\" in Table 18\n",
        "THETA_SIZE = INPUT_SIZE + HORIZON\n",
        "\n",
        "INPUT_SIZE, THETA_SIZE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4PuEm0xiMIdI"
      },
      "source": [
        "### Getting ready for residual connections\n",
        "\n",
        "Beautiful! Hyperparameters ready, now before we create the N-BEATS model, there are two layers to go through which play a large roll in the architecture.\n",
        "\n",
        "They're what make N-BEATS **double residual stacking** (section 3.2 of the [N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf)) possible:\n",
        "* [`tf.keras.layers.subtract(inputs)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/subtract) - subtracts list of input tensors from each other\n",
        "* [`tf.keras.layers.add(inputs)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/add) - adds list of input tensors to each other\n",
        "\n",
        "Let's try them out."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZ_BEvNB--8J",
        "outputId": "c2b460ba-0b82-4f05-8a37-e3b4542a6eaa"
      },
      "source": [
        "# Make tensors\n",
        "tensor_1 = tf.range(10) + 10\n",
        "tensor_2 = tf.range(10)\n",
        "\n",
        "# Subtract\n",
        "subtracted = layers.subtract([tensor_1, tensor_2])\n",
        "\n",
        "# Add\n",
        "added = layers.add([tensor_1, tensor_2])\n",
        "\n",
        "print(f\"Input tensors: {tensor_1.numpy()} & {tensor_2.numpy()}\")\n",
        "print(f\"Subtracted: {subtracted.numpy()}\")\n",
        "print(f\"Added: {added.numpy()}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input tensors: [10 11 12 13 14 15 16 17 18 19] & [0 1 2 3 4 5 6 7 8 9]\n",
            "Subtracted: [10 10 10 10 10 10 10 10 10 10]\n",
            "Added: [10 12 14 16 18 20 22 24 26 28]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XXj9SLYOSVb"
      },
      "source": [
        "Both of these layer functions are straight-forward, subtract or add together their inputs.\n",
        "\n",
        "And as mentioned before, they're what powers N-BEATS double residual stacking.\n",
        "\n",
        "The power of **residual stacking** or **residual connections** was revealed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) where the authors were able to build a deeper but less complex neural network (this is what introduced the popular [ResNet architecture](https://en.wikipedia.org/wiki/Residual_neural_network)) than previous attempts. \n",
        "\n",
        "This deeper neural network led to state of the art results on the ImageNet challenge in 2015 and different versions of residual connections have been present in deep learning ever since.\n",
        "\n",
        "> What is a residual connection?\n",
        "\n",
        "A **residual connection** (also called skip connections) involves a deeper neural network layer receiving the outputs as well as the inputs of a shallower neural network layer.\n",
        "\n",
        "In the case of N-BEATS, the architecture uses residual connections which:\n",
        "* Subtract the backcast outputs from a previous block from the backcast inputs to the current block\n",
        "* Add the forecast outputs from all blocks together in a stack \n",
        "\n",
        "![N-BEATS architecture double residual stacking](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-nbeats-architecture-double-residual-stacking.png)\n",
        "*Annotated version of Figure 1 from the N-BEATS paper highlighting the double residual stacking (section 3.2) of the architecture. Backcast residuals of each block are subtracted from each other and used as the input to the next block where as the forecasts of each block are added together to become the stack forecast.*\n",
        "\n",
        "> What are the benefits of residual connections?\n",
        "\n",
        "In practice, residual connections have been beneficial for training deeper models (N-BEATS reaches ~150 layers, also see \"These approaches provide clear advantages in improving the trainability of deep architectures\" in section 3.2 of the [N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf)).  \n",
        "\n",
        "It's thought that they help avoid the problem of [vanishing gradients](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) (patterns learned by a neural network not being passed through to deeper layers).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_B4LTaqBL9v"
      },
      "source": [
        "### Building, compiling and fitting the N-BEATS algorithm \n",
        "\n",
        "Okay, we've finally got all of the pieces of the puzzle ready for building and training the N-BEATS algorithm.\n",
        "\n",
        "We'll do so by going through the following:\n",
        "\n",
        "1. Setup an instance of the N-BEATS block layer using `NBeatsBlock` (this'll be the initial block used for the network, the rest will be created as part of stacks)\n",
        "2. Create an input layer for the N-BEATS stack (we'll be using the [Keras Functional API](https://www.tensorflow.org/guide/keras/functional) for this)\n",
        "3. Make the initial backcast and forecasts for the model with the layer created in (1)\n",
        "4. Use a for loop to create stacks of block layers\n",
        "5. Use the NBeatsBlock class within the for loop created in (4) to create blocks which return backcasts and block-level forecasts\n",
        "6. Create the double residual stacking using subtract and add layers\n",
        "7. Put the model inputs and outputs together using [`tf.keras.Model()`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)\n",
        "8. Compile the model with MAE loss (the paper uses multiple losses but we'll use MAE to keep it inline with our other models) and Adam optimizer with default settings as per section 5.2 of [N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf))\n",
        "9. Fit the N-BEATS model for 5000 epochs and since it's fitting for so many epochs, we'll use a couple of callbacks:\n",
        "  * [`tf.keras.callbacks.EarlyStopping()`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping) - stop the model from training if it doesn't improve validation loss for 200 epochs and restore the best performing weights using `restore_best_weights=True` (this'll prevent the model from training for loooongggggg period of time without improvement)\n",
        "  * [`tf.keras.callbacks.ReduceLROnPlateau()`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ReduceLROnPlateau) - if the model's validation loss doesn't improve for 100 epochs, reduce the learning rate by 10x to try and help it make incremental improvements (the smaller the learning rate, the smaller updates a model tries to make)\n",
        "\n",
        "Woah. A bunch of steps. But I'm sure you're up to it.\n",
        "\n",
        "Let's do it!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2U3-Wv94-hfQ",
        "outputId": "db5485a6-9ab8-47ee-9b52-3dd5ff683725"
      },
      "source": [
        " %%time\n",
        "\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# 1. Setup N-BEATS Block layer\n",
        "nbeats_block_layer = NBeatsBlock(input_size=INPUT_SIZE,\n",
        "                                 theta_size=THETA_SIZE,\n",
        "                                 horizon=HORIZON,\n",
        "                                 n_neurons=N_NEURONS,\n",
        "                                 n_layers=N_LAYERS,\n",
        "                                 name=\"InitialBlock\")\n",
        "\n",
        "# 2. Create input to stacks\n",
        "stack_input = layers.Input(shape=(INPUT_SIZE), name=\"stack_input\")\n",
        "\n",
        "# 3. Create initial backcast and forecast input (backwards predictions are referred to as residuals in the paper)\n",
        "backcast, forecast = nbeats_block_layer(stack_input)\n",
        "# Add in subtraction residual link, thank you to: https://github.com/mrdbourke/tensorflow-deep-learning/discussions/174 \n",
        "residuals = layers.subtract([stack_input, backcast], name=f\"subtract_00\") \n",
        "\n",
        "# 4. Create stacks of blocks\n",
        "for i, _ in enumerate(range(N_STACKS-1)): # first stack is already creted in (3)\n",
        "\n",
        "  # 5. Use the NBeatsBlock to calculate the backcast as well as block forecast\n",
        "  backcast, block_forecast = NBeatsBlock(\n",
        "      input_size=INPUT_SIZE,\n",
        "      theta_size=THETA_SIZE,\n",
        "      horizon=HORIZON,\n",
        "      n_neurons=N_NEURONS,\n",
        "      n_layers=N_LAYERS,\n",
        "      name=f\"NBeatsBlock_{i}\"\n",
        "  )(residuals) # pass it in residuals (the backcast)\n",
        "\n",
        "  # 6. Create the double residual stacking\n",
        "  residuals = layers.subtract([residuals, backcast], name=f\"subtract_{i}\") \n",
        "  forecast = layers.add([forecast, block_forecast], name=f\"add_{i}\")\n",
        "\n",
        "# 7. Put the stack model together\n",
        "model_7 = tf.keras.Model(inputs=stack_input, \n",
        "                         outputs=forecast, \n",
        "                         name=\"model_7_N-BEATS\")\n",
        "\n",
        "# 8. Compile with MAE loss and Adam optimizer\n",
        "model_7.compile(loss=\"mae\",\n",
        "                optimizer=tf.keras.optimizers.Adam(0.001),\n",
        "                metrics=[\"mae\", \"mse\"])\n",
        "\n",
        "# 9. Fit the model with EarlyStopping and ReduceLROnPlateau callbacks\n",
        "model_7.fit(train_dataset,\n",
        "            epochs=N_EPOCHS,\n",
        "            validation_data=test_dataset,\n",
        "            verbose=0, # prevent large amounts of training outputs\n",
        "            # callbacks=[create_model_checkpoint(model_name=stack_model.name)] # saving model every epoch consumes far too much time\n",
        "            callbacks=[tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=200, restore_best_weights=True),\n",
        "                      tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\", patience=100, verbose=1)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 00328: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00428: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "CPU times: user 1min 23s, sys: 4.58 s, total: 1min 28s\n",
            "Wall time: 3min 44s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j_nv-mjqHsOi"
      },
      "source": [
        "And would you look at that! N-BEATS algorithm fit to our Bitcoin historical data.\n",
        "\n",
        "How did it perform?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pyI7WKpbo7cR",
        "outputId": "26c5c6c5-14df-45fb-cbbf-a862a2eeec87"
      },
      "source": [
        "# Evaluate N-BEATS model on the test dataset\n",
        "model_7.evaluate(test_dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 46ms/step - loss: 585.4998 - mae: 585.4998 - mse: 1179491.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[585.4998168945312, 585.4998168945312, 1179491.5]"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTVDsIs31alx",
        "outputId": "5186bd4d-5eee-44a4-b87a-42dfb3e40502"
      },
      "source": [
        "# Make predictions with N-BEATS model\n",
        "model_7_preds = make_preds(model_7, test_dataset)\n",
        "model_7_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([8908.059, 8854.672, 8990.933, 8759.821, 8819.711, 8774.012,\n",
              "       8604.187, 8547.038, 8495.928, 8489.514], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VL-cncpQ1i7C",
        "outputId": "912b5017-0a8d-4458-91c8-1b2ae545e142"
      },
      "source": [
        "# Evaluate N-BEATS model predictions\n",
        "model_7_results = evaluate_preds(y_true=y_test,\n",
        "                                 y_pred=model_7_preds)\n",
        "model_7_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 585.4998,\n",
              " 'mape': 2.7445195,\n",
              " 'mase': 1.028561,\n",
              " 'mse': 1179491.5,\n",
              " 'rmse': 1086.044}"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNT-2qEfLX_D"
      },
      "source": [
        "Woah... even with all of those special layers and hand-crafted network, it looks like the N-BEATS model doesn't perform as well as `model_1` or the original naive forecast.\n",
        "\n",
        "This goes to show the power of smaller networks as well as the fact not all larger models are better suited for a certain type of data.\n",
        "\n",
        "### Plotting the N-BEATS architecture we've created\n",
        "\n",
        "You know what would be cool?\n",
        "\n",
        "If we could plot the N-BEATS model we've crafted.\n",
        "\n",
        "Well it turns out we can using [`tensorflow.keras.utils.plot_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/plot_model).\n",
        "\n",
        "Let's see what it looks like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNouVhCuI-99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b1d03734-4939-4e7f-9091-67455b09389b"
      },
      "source": [
        "# Plot the N-BEATS model and inspect the architecture\n",
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model_7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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GDB04cEARERGmoyCXBAUFKSoqSsHBwVcU6YcPH5bD4TCYDLiaw+HQli1bVLduXdNRAAAAAABuhhIdAAAAcEMpKSkaN26cXnjhBZUvX950HOSiUqVKae3atapQoUJGke5wOHTo0CHDyYAr7dy5U2fOnFGDBg1MRwEAAAAAuBlKdAAAAMANTZkyRUlJSRo6dKjpKDCgZMmSWrNmjUJCQuTp6SlJiouLMxsK+IeYmBjlz59f1apVMx0FAAAAAOBmvEwHAAAAAJC7Tp8+rYkTJ6pfv34qWbJklt4TGRmZw6lgwsCBAzVy5EjFx8crMjJSCQkJpiMhj2rcuLHKli2brfu02+2qW7euvLz40wUAAAAAIHfZLMuyTIcAAAAAkHsiIiI0atQo7d+/X0WKFMnSe2w2Ww6nApCXzZ07V507d87Wfd5zzz164IEHFBERka37BQAAAADgBubxdW4AAADAjaSmpmratGnq1atXlgv0y3KiJINzOHnypObPn6+ePXuajoI8KCe+ZJOSkqKdO3dqyJAh2b5vAAAAAABuhGeiAwAAAG5k/vz5Onz4sPr27Ws6CpxIkSJFKNDhVDZu3KjU1FTVr1/fdBQAAAAAgBuiRAcAAADcyIwZM9S2bdtsf3YxAGQnu92uwMBAhYSEmI4CAAAAAHBDLOcOAAAAuIk///xTq1ev1uLFi01HAYDrstvtzEIHAAAAABjDTHQAAADATfz3v/9VyZIl1aZNG9NRAOC6YmJiVK9ePdMxAAAAAABuihIdAAAAcBPz5s1Tx44d5eXFglQAnNeJEye0d+9eSnQAAAAAgDGU6AAAAIAb+PPPP7Vt2zZ17NjRdBQAuK7o6GhZlqXQ0FDTUQAAAAAAbooSHQAAAHADy5cvV+HChdWsWTPTUQDgumJiYlShQgWVKFHCdBQAAAAAgJuiRAcAAADcwI8//qj77ruPpdwBOD273a769eubjgEAAAAAcGOU6AAAAICLsyxLa9as0QMPPGA6CgDckN1u53noAAAAAACjKNEBAAAAFxcbG6vjx4+rcePGpqMAwHXFxcXpyJEjzEQHAAAAABhFiQ4AAAC4uI0bN8rb21s1a9Y0HQUArismJkaenp665557TEcBAAAAALgxSnQAAADAxW3evFnVq1eXr6+v6SgAcF12u1133323ChQoYDoKAAAAAMCNUaIDAAAALu6PP/5QlSpVTMcAgBuKiYlhKXcAAAAAgHGU6AAAAICL27NnjypVqmQ6BgBcl8Ph0KZNm1SvXj3TUQAAAAAAbo4SHQAAAHBx+/btU0hISK4db8GCBapQoYJsNptsNpv+9a9/XTWmVatWKlSokDw9PVW9enVt2rTpqvdd/vHy8lLx4sXVsmVLLVy4MNd+j8zcSsaIiAiVKFFCNptNH3/8cbbmGT9+vPz9/WWz2bRly5Zs3Xd6eromT56sxo0bZ2n8+fPnVaVKFQ0fPvymj8U14xrXzO3auXOnzpw5w0x0AAAAAIBxlOgAAACAC0tOTta5c+d0xx135NoxO3XqpL1796pixYoqVqyYvvjiC3333XdXjFmxYoXmzZundu3aaefOnapTp84V7/P395dlWbIsS0ePHtXcuXN18OBBderUSXPnzs213+WfbiXj66+/rvXr1+dInqFDh+qTTz7J9v3GxsbqvvvuU//+/XXu3LksveeNN97Q7t27b+l4XDN5/5rJDjExMcqfP7+qV69uOgoAAAAAwM1RogMAAAAu7MiRI5KkkiVLGjn+tGnT5OHhod69eyspKemW9lGkSBE98MADmjp1qiQpMjIyOyMqJSUly7OtryWnM+amrVu3asiQIXrxxRdVu3btLL1n/fr12rFjR7Ycn2vGfdntdtWtW1deXl6mowAAAAAA3BwlOgAAAODCTpw4IUkqWrSokeM3btxYffv21cGDB/X666/f1r6Cg4MlSadOncqGZP/v008/zfiywe3KqYy5qVatWlqwYIGeeuop+fj43HB8SkqKBg4cqClTpmTL8blm3Jfdbud56AAAAAAAp0CJDgAAALiwlJQUSVL+/PmNZRgzZozuuusuzZw5Uz/88MMt72fbtm2SpGbNml3xusPh0Jtvvqly5crJz89PNWvWvGJp7LVr16patWry9/eXr6+vatSooeXLl0uS+vbtqwEDBujPP/+UzWZTpUqVJElr1qxR/fr1lT9/fhUuXFg1atRQcnLyLWfMjGVZmjRpkqpWrSofHx8VKVJEjz32mH7//ferxs6ePVuhoaHy9fVVgQIFFBwcrNGjR2e638TERAUHB8vLy0tt2rS5YY7b9cYbb+jll19WYGBgptuXLVumwoULa+zYsVneJ9dM5lzlmslMSkqKduzYQYkOAAAAAHAKlOgAAACAC7v8PGs/Pz9jGfz8/PTZZ5/Jw8NDPXv21NmzZ2/q/SkpKVq2bJlef/11tWrVSgMGDLhi+5AhQ/TOO+9o8uTJOnTokNq1a6cnn3xSGzZskPR3QdilSxfFxcUpISFBBQsW1FNPPSVJmjJlitq1a6eKFSvKsizt2bNHZ8+eVfv27RUWFqYTJ04oNjZWd911ly5evHjLGTMTHh6uoUOH6o033tCRI0f0888/66+//lLTpk2VmJiYMW7KlCnq3r27wsLClJCQoPj4eA0bNuyazx8vWrSoQkNDtXDhQi1btuyGOW7HunXr9Oeff+rJJ5+85hiHwyFJSk9Pz/J+uWYy5wrXzLVs2rRJqampql+/vpHjAwAAAADwvyjRAQAAABdmWZbpCJKkRo0aqV+/foqLi9OQIUNuOD4pKUk2m002m0358+fXQw89pMKFC+upp56St7d3xrjz58/rww8/VIcOHdSpUycFBARo+PDh8vb21qxZsyRJYWFheuutt1SkSBEVLVpU7du31/Hjx3X06NFMjx0XF6fk5GRVr15dvr6+KlmypBYsWKDixYvfUsbMpKSkaNKkSerYsaO6desmf39/1ahRQx9//LGOHTum6dOnS5JSU1M1cuRItWjRQkOGDFHRokVVpEgRPf/885nO2E1LS9PTTz+tHj16qH379jf8nG9HSkqK+vbtqw8//PC649q2bavk5GSNGDHipvbPNXMlV7hmricmJkbFihVTSEiIsQwAAAAAAFxGiQ4AAAC4sMsz0M+fP284yd9LdFeuXFkffPCBfvnll+uO9ff3l2VZsixLqampio+PV79+/fTaa6+pZs2aOnbsmCRp9+7dOnfunO6+++6M9/r5+alUqVKZLnEtKaOsvDxD+p8qVKigEiVKqFu3bgoPD1dcXNxtZczMzp07debMGYWGhl7xer169ZQvXz5FR0dL+nup71OnTql169ZXjPP09FSfPn2ueM3hcOjJJ59UiRIlcmVJ7mHDhqlXr14qU6ZMjh2Da+b/ucI1cz12u10NGjSQzWYzmgMAAAAAAIkSHQAAAHBpl5+FfnlZd5N8fX01a9Ys2Ww2PffccxnPa78RLy8vlSlTRs8++6wiIiK0e/dujR8/XpIylvkePnx4xgxfm82m/fv3Z/zO3333nZo3b67AwED5+Pho0KBB1z2en5+ffvrpJzVp0kRjx45VhQoV1LVr1+vmvV7GzJw6dUqSVLBgwau2BQQE6PTp05KU8UztgICA62aWpFdeeUWxsbH6+OOPtWvXrhuOvx2//PKLtm/frh49euTocbhm/l9ev2ZuJCYmhuehAwAAAACcBiU6AAAA4MIuz0TPavmY0xo1aqT+/fsrNjZWo0ePvun316hRQ5IyCr/AwEBJ0uTJkzNm+F7++fXXX3XgwAF16NBBpUqVUnR0tJKSkjRhwoQbHqd69er69ttvlZCQoMGDB2vu3LmKiIi4pYyZuVxwXi4+/9epU6dUtmxZSdIdd9whSdedoXxZ586dtXLlSgUEBKh79+5KS0vLUt5b8emnn+rHH3+Uh4dHRgl9+VyMHTtWNpst4/nit4tr5m95/Zq5nhMnTmjv3r08Dx0AAAAA4DQo0QEAAAAXdnkmurOU6JI0evRoValSRZs3b77p927cuFGSVLlyZUlSUFCQfH19tWXLlkzHb9++XampqXrppZdUoUIF+fr63nC56ISEhCsK1/Hjx6tOnTpZnqn7z4yZufvuu1WwYMGriubo6GhdvHhRdevWlSQFBweraNGiWrFixQ2P26JFCxUvXlzTp0/Xxo0bNWbMmCzlvRWzZs26qoC+/LzwN954Q5ZlXbXs+O3gmsn718z1xMTEZPs1AwAAAADA7aBEBwAAAFzY5RL98hLWzuDyEt2enp7XHZeSkqL09HRZlqWEhATNmjVLw4cPV/HixdWvX7+MfT377LP673//qw8//FDJyclyOByKj4/XoUOHVK5cOUnSDz/8oPPnzys2Njbj2dGXFS1aVAkJCYqLi9Pp06e1f/9+vfDCC/r999918eJFbd68Wfv371fDhg1vKeO1PoMBAwZo4cKF+uKLL5ScnKzt27frxRdfVOnSpdW7d29Jko+Pj4YNG6aff/5Zr732mg4ePKj09HSdPn36mgVt+/bt9cwzz2js2LEZ5axJ33//vQoXLqyxY8fe8j64Zlz7momJiVFISIhKlCiR68cGAAAAACBTFgAAAACXdfHiRcvT09OKjIy8rf1IsubOnZulsQsXLrQqVqxoSbKKFy9uvfLKK5mOGzhwoPXoo49m+r5//vj4+Fh33nmn9dJLL1n/x96dhlVVLmwcvzeDTE6YYxbO84QymXOTpkWp4JCZJ0+jRxNzgtSTdkxNy9JSM8tyykTT6jgcMs0hRWCTsx7NCVPJkdRKRGSv90PGm8cJEXg28P9dFx/c61lr3e61l5dw8zzrp59+uuo4aWlpVmRkpOXn52e5ublZZcqUscLCwqxdu3ZZlmVZkZGRVqlSpaySJUtaXbp0saZMmWJJsqpVq2b99NNP1ubNm61KlSpZXl5eVosWLaz4+HirWbNmlq+vr+Xq6mrdfffd1vDhw63Lly9nK+PEiROtcuXKWZIsHx8fq3PnzpZlWZbD4bDeeustq0aNGpa7u7vl6+trderUydq7d+8179WUKVOsBg0aWJ6enpanp6fVuHFja+rUqdbixYstX19fS5JVuXJl6+TJk9b58+ete++915JkFS1a1JozZ06WrtufNm3aZDVv3tyqUKFC5t+tfPnyVrNmzax169bdcL9Tp05Zkqzhw4df9fqKFSusYsWKWW+88cYN9+Uzk78/M7fz78P1PPbYY1a3bt2yvT8AAAAAADlsoc2yLCuXe3oAAAAABlWqVEl9+/bV0KFDs30Mm82m6Ohode3aNQeTASgI7vTfh/Lly2vIkCEaNGhQDicDAAAAACBbFrGcOwAAAFDAValSRYcOHTIdAwCukZSUpBMnTig4ONh0FAAAAAAAMlGiAwAAAAUcJXrhtmfPHtlstlt+de/e3XRUFEIJCQlydXVVkyZNTEcBAAAAACCTm+kAAAAAAHJXlSpVtGnTJtMxYEjt2rXFU7zgrOx2u+rXry8fHx/TUQAAAAAAyMRMdAAAAKCAq1atmpKSkpSenm46CgBcJSEhgaXcAQAAAABOhxIdAAAAKOCaNGmitLQ07dixw3QUAMjkcDi0ZcsWBQUFmY4CAAAAAMBVKNEBAACAAq527doqWbKk4uPjTUcBgEy7du3Sr7/+SokOAAAAAHA6lOgAAABAAWez2RQQEKCEhATTUQAgU0JCgry9vVW/fn3TUQAAAAAAuAolOgAAAFAIhISEUKIDcCp2u11NmjSRm5ub6SgAAAAAAFyFEh0AAAAoBIKDg/Xf//5XZ8+eNR0FACT9MRM9ODjYdAwAAAAAAK5BiQ4AAAAUAk2bNpUkbdy40XASAJBSU1O1c+dOnocOAAAAAHBKlOgAAABAIVCuXDk1adJEK1asMB0FALR582alp6czEx0AAAAA4JQo0QEAAIBCokOHDlq2bJnpGAAgu92uUqVKqUqVKqajAAAAAABwDUp0AAAAoJB49NFH9dNPP2nXrl2mowAo5Ox2u0JCQmSz2UxHAQAAAADgGpToAAAAQCERFBSkcuXKafny5aajACjkEhISWModAAAAAOC0KNEBAACAQsLFxUWPPPIIS7oDMColJUUHDhxQUFCQ6SgAAAAAAFwXJToAAABQiHTs2FEbN27U0aNHTUcBUEglJCTIsixKdAAAAACA06JEBwAAAAqRDh06yNfXV/PnzzcdBUAhlZCQoCpVqqhs2bKmowAAAAAAcF1upgMAAAAAyDtFihRRly5dNHv2bA0dOvS29t20aVMupUJusSxLNpvNdIwcl5GRIVdXV9MxkE12u53noQMAAAAAnJrNsizLdAgAAAAAeSc2NlbNmzfXli1b5O/vn6V9CmIRCyDnREdHq2vXrlkaW758eQ0ePFiDBw/O5VQAAAAAAGTLIpZzBwAAAAqZZs2aqUaNGpo7d26W97Esi6989LVkyRK5urpq3LhxxrPkxtfs2bPl4+Ojli1b6tixY8bz8GVluUBPSkrSiRMnmIkOAAAAAHBqlOgAAABAIdSjRw999tlnunTpkukoyGGrV6/Wk08+qT59+igqKsp0nFzRq1cv2e12paSkqFGjRvrmm29MR0IWxcXFyc3NTQEBAaajAAAAAABwQ5ToAAAAQCH0/PPPKyUlRQsXLjQdBTkoISFBHTt2VHh4uCZPnmw6Tq6qU6eO4uLi9NBDD6l9+/aKiopSRkaG6Vi4hfj4eDVs2FA+Pj6mowAAAAAAcEOU6AAAAEAhVLFiRXXq1EmTJk0yHQU5ZN++fQoNDVWbNm00a9YsubgU/G/3ihYtqs8//1yzZs3Se++9p4cffljHjx83HQs3ERcXp6ZNm5qOAQAAAADATRX8n6oAAAAAuK6XX35ZP/zwg+Li4kxHwR06evSoHn74YVWtWlULFiyQm5ub6Uh5qlevXtqwYYMOHz6swMBAbdy40XQkXEdaWpq2bNmikJAQ01EAAAAAALgpSnQAAACgkGrRooUCAwP1/vvvm46CO3D69Gm1bdtWxYoV04oVKwrtMtlNmjTRli1b1LRpU7Vp00ajRo2Sw+EwHQt/sXXrVqWlpTETHQAAAADg9CjRAQAAgEKsb9+++uKLL/Tzzz+bjoJsuHDhgh5//HGlpaVp5cqV8vX1NR3JqOLFi2vRokV6++23NXbsWHXs2FG//PKL6Vi4Ii4uTqVKlVKNGjVMRwEAAAAA4KYo0QEAAIBCrHv37ipVqpTeeecd01Fwmy5duqTOnTtr//79WrFihSpUqGA6klOw2WyKiIjQ6tWrtXnzZvn7+ys+Pt50LEiKj49XSEiIbDab6SgAAAAAANwUJToAAABQiHl6emrQoEH64IMPdPLkSdNxkEUOh0NPP/20Nm3apJiYGNWqVct0JKfTsmVLbd26VXXq1FHr1q01efJk05EKvbi4OJ6HDgAAAADIFyjRAQAAgEKuT58+8vHx0aRJk0xHQRa98sor+ve//61///vfatKkiek4Tqt06dL6z3/+o9dff12DBg1SWFiYzp07ZzpWoXTy5EkdOnSI56EDAAAAAPIFSnQAAACgkPPx8dErr7yi999/X6dPnzYdB7cwYsQITZ06VfPmzVPr1q1Nx3F6NptNkZGR+vbbbxUbG6vg4GDt2LHDdKxCJy4uTjabTUFBQaajAAAAAABwS5ToAAAAAPTyyy/Ly8tL77//vukouIlp06Zp7Nix+vDDDxUWFmY6Tr5y//33KzExUWXLllVwcLA+/vhj05EKlfj4eNWqVUulSpUyHQUAAAAAgFuiRAcAAAAgHx8fRUREaPLkyTpz5ozpOLiOzz//XC+//LLGjx+vZ5991nScfKlixYpas2aNIiMj9eKLL6pXr166cOGC6ViFQlxcHEu5AwAAAADyDUp0AAAAAJKkiIgIeXt7a/To0aaj4H+sWrVKvXv3Vt++fTVkyBDTcfI1Nzc3jRo1Sl999ZWWL1+uoKAg7d6923SsAs3hcCgxMVEhISGmowAAAAAAkCWU6AAAAAAkSUWLFtXIkSM1bdo07du3z3QcXBEfH69OnTqpS5cumjx5suk4BUZoaKi2bNmi4sWLq2nTpoqOjjYdqcDatWuXzp8/T4kOAAAAAMg3KNEBAAAAZHruuedUs2ZNDR8+3HQU6I/ysUOHDrr//vv16aefymazmY5UoPj5+Wnt2rXq3bu3unfvrhdffFGXLl0yHavAiYuLk7e3txo0aGA6CgAAAAAAWUKJDgAAACCTq6urxo0bp0WLFmnjxo2m4xRqR44cUYcOHVSrVi0tWLBAbm5upiMVSB4eHpo8ebKWLFmi6OhoNWvWTIcOHTIdq0CJj49XUFAQn2EAAAAAQL5BiQ4AAADgKqGhoWrdurUiIyNlWZbpOIXS6dOn1bZtW5UoUULLly+Xt7e36UgFXqdOnZSQkKD09HQ1btxYS5YsMR2pwIiPj2cpdwAAAABAvkKJDgAAAOAaEydO1KZNmzRv3jzTUQqdX3/9VY888ojS09O1cuVK+fr6mo5UaNSsWVPx8fHq2rWrwsPDFRERofT0dNOx8rVff/1V//3vfynRAQAAAAD5CiU6AAAAgGsEBATohRde0JAhQ3T27FnTcQqNS5cuKTw8XEeOHNGKFStUvnx505EKHU9PT82YMUOzZs3Sxx9/rIceekjJycmmY+Vb8fHxysjIoEQHAAAAAOQrlOgAAAAArmvs2LGyLEsjR440HaVQyMjIUM+ePRUfH6+YmBjVrFnTdKRCrVevXkpMTNTp06fl7++vlStXmo6UL8XHx8vPz08VK1Y0HQUAAAAAgCyjRAcAAABwXb6+vnrzzTc1depUbdmyxXScAs2yLPXp00fLli3T0qVL1bhxY9ORIKlOnTqKi4vTAw88oA4dOmjUqFFyOBymY+UrPA8dAAAAAJAfUaIDAAAAuKFnnnlGTZs2Vb9+/SgPc9GwYcP0ySefaN68eWrZsqXpOPiLYsWKacGCBZo2bZrGjRunhx9+WCdOnDAdK99ISEigRAcAAAAA5DuU6AAAAABuyGazaerUqUpISND06dNNxymQpkyZovHjx2vGjBnq3Lmz6Ti4gRdeeEGxsbE6dOiQAgMDtXHjRtORnN6BAwd04sQJNW3a1HQUAAAAAABuCyU6AAAAgJtq1KiRIiMjNXToUB08eNB0nALls88+U0REhN566y39/T7ag8sAACAASURBVO9/Nx0HtxAQEKAtW7YoODhYbdq00fjx42VZlulYTis+Pl7u7u5q0qSJ6SgAAAAAANwWm8V3/AAAAABuIS0tTQEBASpXrpxWrVolm81mOlK+t2zZMnXq1ElDhgzR2LFjTcfBbbAsS++9956GDBmi9u3ba9asWfL19TUdy+lEREQoNjZWdrvddBQAAAAAAG7HImaiAwAAALglDw8PzZw5U+vWrdOnn35qOk6+FxcXp+7du6tHjx4aM2aM6Ti4TTabTREREVq1apUSExPVuHFjJSQkmI7ldOLi4ljKHQAAAACQL1GiAwAAAMiSkJAQRUREaNCgQUpOTjYdJ9/auXOnOnTooAcffFAzZ85kVn8+1qpVK23dulW1atVSq1atNHnyZNORnEZaWpq2bdumkJAQ01EAAAAAALhtlOgAAAAAsmz06NEqU6aMnn32WZ4FnQ0HDx5U27Zt1ahRI0VHR8vNzc10JNyhMmXKKCYmRq+//roGDhyonj176rfffjMdy7jNmzcrLS2NmegAAAAAgHyJEh0AAABAlnl7e2v+/PlavXq1pkyZYjpOvnLq1Cl16NBBZcqU0ZdffilPT0/TkZBDbDabIiMj9e2332rVqlUKDAzUjh07TMcyKi4uTnfddZeqVatmOgoAAAAAALeNEh0AAADAbQkMDNSwYcM0dOjQQl8UZtX58+f1yCOPKCMjQytXrlTJkiVNR0IueOCBB5SYmKjSpUsrJCREM2fONB3JmPj4eDVt2pTHFQAAAAAA8iVKdAAAAAC3bcSIEWrcuLF69Oihixcvmo7j1FJTUxUaGqrjx4/r22+/Vbl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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBtHpyXhHhkU"
      },
      "source": [
        "Now that is one good looking model! \n",
        "\n",
        "It even looks similar to the model shown in Figure 1 of the N-BEATS paper. \n",
        "\n",
        "![N-BEATS replica model compared with actual N-BEATS architecture](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-nbeats-stack-model-vs-nbeats-architecture.png)\n",
        "*Comparison of `model_7` (N-BEATS replica model make with Keras Functional API) versus actual N-BEATS architecture diagram.*\n",
        "\n",
        "Looks like our Functional API usage did the trick! \n",
        "\n",
        "> 🔑 **Note:** Our N-BEATS model replicates the N-BEATS **generic architecture**, the training setups are largely the same, except for the N-BEATS paper used an ensemble of models to make predictions (multiple different loss functions and multiple different lookback windows), see Table 18 of the [N-BEATS paper](https://arxiv.org/pdf/1905.10437.pdf) for more. An extension could be to setup this kind of training regime and see if it improves performance.\n",
        "\n",
        "How about we try and save our version of the N-BEATS model?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_mxaHYeShwu",
        "outputId": "926049da-10b6-40ba-9649-35dc9635b34d"
      },
      "source": [
        "# This will error out unless a \"get_config()\" method is implemented - this could be extra curriculum\n",
        "model_7.save(model_7.name)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as theta_layer_call_and_return_conditional_losses, theta_layer_call_fn, theta_layer_call_and_return_conditional_losses, theta_layer_call_fn, theta_layer_call_and_return_conditional_losses while saving (showing 5 of 750). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_7_N-BEATS/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_7_N-BEATS/assets\n",
            "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8JOysiNUCML"
      },
      "source": [
        "You'll notice a warning appears telling us to fully save our model correctly we need to implement a [`get_config()`](https://www.tensorflow.org/guide/keras/save_and_serialize#how_savedmodel_handles_custom_objects) method in our custom layer class.\n",
        "\n",
        "> 📖 **Resource:** If you would like to save and load the N-BEATS model or any other custom or subclassed layer/model configuration, you should overwrite the `get_config()` and optionally `from_config()` methods. See the [TensorFlow Custom Objects documentation](https://www.tensorflow.org/guide/keras/save_and_serialize#custom_objects) for more."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9k10r8BN7ELS"
      },
      "source": [
        "## Model 8: Creating an ensemble (stacking different models together)\n",
        "\n",
        "After all that effort, the N-BEATS algorithm's performance was underwhelming.\n",
        "\n",
        "But again, this is part of the parcel of machine learning. Not everything will work.\n",
        "\n",
        "That's when we refer back to the motto: experiment, experiment, experiment.\n",
        "\n",
        "Our next experiment is creating an [**ensemble** of models](https://en.wikipedia.org/wiki/Ensemble_learning).\n",
        "\n",
        "An **ensemble** involves training and combining multiple different models on the same problem. Ensemble models are often the types of models you'll see winning data science competitions on websites like Kaggle.\n",
        "\n",
        "![high level overview of ensemble model: combining many different models may result in better results than one single model](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-ensemble-model-example.png)\n",
        "*Example of the power of ensembling. One Daniel model makes a decision with a smart level of 7 but when a Daniel model teams up with multiple different people, together (ensembled) they make a decision with a smart level of 10. The key here is combining the decision power of people with different backgrounds, if you combined multiple Daniel models, you'd end up with an average smart level of 7. Note: smart level is not an actual measurement of decision making, it is for demonstration purposes only.*\n",
        "\n",
        "For example, in the N-BEATS paper, they trained an ensemble of models (180 in total, see [section 3.4](https://arxiv.org/pdf/1905.10437.pdf)) to achieve the results they did using a combination of:\n",
        "  * Different loss functions (sMAPE, MASE and MAPE)\n",
        "  * Different window sizes (2 x horizon, 3 x horizon, 4 x horizon...)\n",
        "\n",
        "The benefit of ensembling models is you get the \"decision of the crowd effect\". Rather than relying on a single model's predictions, you can [take the average or median of many different models](https://en.wikipedia.org/wiki/Ensemble_forecasting).\n",
        "\n",
        "The keyword being: different.\n",
        "\n",
        "It wouldn't make sense to train the same model 10 times on the same data and then average the predictions.\n",
        "\n",
        "Fortunately, due to their random initialization, even deep learning models with the same architecture can produce different results. \n",
        "\n",
        "What I mean by this is each time you create a deep learning model, it starts with random patterns (weights & biases) and then it adjusts these random patterns to better suit the dataset it's being trained on. \n",
        "\n",
        "However, the process it adjusts these patterns is often a form of guided randomness as well (the SGD optimizer stands for stochastic or random gradient descent).\n",
        "\n",
        "To create our ensemble models we're going to be using a combination of: \n",
        "* Different loss functions (MAE, MSE, MAPE)\n",
        "* Randomly initialized models \n",
        "\n",
        "Essentially, we'll be creating a suite of different models all attempting to model the same data.\n",
        "\n",
        "And hopefully the combined predictive power of each model is better than a single model on its own.\n",
        "\n",
        "Let's find out!\n",
        "\n",
        "We'll start by creating a function to produce a list of different models trained with different loss functions. Each layer in the ensemble models will be initialized with a random normal [(Gaussian) distribution](https://en.wikipedia.org/wiki/Normal_distribution) using [He normal initialization](https://www.tensorflow.org/api_docs/python/tf/keras/initializers/HeNormal), this'll help estimating the prediction intervals later on.\n",
        "\n",
        "> 🔑 **Note:** In your machine leanring experiments, you may have already dealt with examples of ensemble models. Algorithms such as the [random forest model](https://towardsdatascience.com/understanding-random-forest-58381e0602d2) are a form of ensemble, it uses a number of randomly created decision trees where each individual tree may perform poorly but when combined gives great results.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sA0LwkPxEyib"
      },
      "source": [
        "### Constructing and fitting an ensemble of models (using different loss functions)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5PHo4O3z50g"
      },
      "source": [
        "def get_ensemble_models(horizon=HORIZON, \n",
        "                        train_data=train_dataset,\n",
        "                        test_data=test_dataset,\n",
        "                        num_iter=10, \n",
        "                        num_epochs=100, \n",
        "                        loss_fns=[\"mae\", \"mse\", \"mape\"]):\n",
        "  \"\"\"\n",
        "  Returns a list of num_iter models each trained on MAE, MSE and MAPE loss.\n",
        "\n",
        "  For example, if num_iter=10, a list of 30 trained models will be returned:\n",
        "  10 * len([\"mae\", \"mse\", \"mape\"]).\n",
        "  \"\"\"\n",
        "  # Make empty list for trained ensemble models\n",
        "  ensemble_models = []\n",
        "\n",
        "  # Create num_iter number of models per loss function\n",
        "  for i in range(num_iter):\n",
        "    # Build and fit a new model with a different loss function\n",
        "    for loss_function in loss_fns:\n",
        "      print(f\"Optimizing model by reducing: {loss_function} for {num_epochs} epochs, model number: {i}\")\n",
        "\n",
        "      # Construct a simple model (similar to model_1)\n",
        "      model = tf.keras.Sequential([\n",
        "        # Initialize layers with normal (Gaussian) distribution so we can use the models for prediction\n",
        "        # interval estimation later: https://www.tensorflow.org/api_docs/python/tf/keras/initializers/HeNormal\n",
        "        layers.Dense(128, kernel_initializer=\"he_normal\", activation=\"relu\"), \n",
        "        layers.Dense(128, kernel_initializer=\"he_normal\", activation=\"relu\"),\n",
        "        layers.Dense(HORIZON)                                 \n",
        "      ])\n",
        "\n",
        "      # Compile simple model with current loss function\n",
        "      model.compile(loss=loss_function,\n",
        "                    optimizer=tf.keras.optimizers.Adam(),\n",
        "                    metrics=[\"mae\", \"mse\"])\n",
        "      \n",
        "      # Fit model\n",
        "      model.fit(train_data,\n",
        "                epochs=num_epochs,\n",
        "                verbose=0,\n",
        "                validation_data=test_data,\n",
        "                # Add callbacks to prevent training from going/stalling for too long\n",
        "                callbacks=[tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\",\n",
        "                                                            patience=200,\n",
        "                                                            restore_best_weights=True),\n",
        "                           tf.keras.callbacks.ReduceLROnPlateau(monitor=\"val_loss\",\n",
        "                                                                patience=100,\n",
        "                                                                verbose=1)])\n",
        "      \n",
        "      # Append fitted model to list of ensemble models\n",
        "      ensemble_models.append(model)\n",
        "\n",
        "  return ensemble_models # return list of trained models"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Oz-iLZhDvSu"
      },
      "source": [
        "Ensemble model creator function created!\n",
        "\n",
        "Let's try it out by running `num_iter=5` runs for 1000 epochs. This will result in 15 total models (5 for each different loss function).\n",
        "\n",
        "Of course, these numbers could be tweaked to create more models trained for longer. \n",
        "\n",
        "> 🔑 **Note:** With ensembles, you'll generally find more total models means better performance. However, this comes with the tradeoff of having to train more models (longer training time) and make predictions with more models (longer prediction time).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcyU88yvz_Zk",
        "outputId": "165a3b49-302b-408f-bb80-277b31cde674"
      },
      "source": [
        "%%time\n",
        "# Get list of trained ensemble models\n",
        "ensemble_models = get_ensemble_models(num_iter=5,\n",
        "                                      num_epochs=1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimizing model by reducing: mae for 1000 epochs, model number: 0\n",
            "\n",
            "Epoch 00794: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00928: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Optimizing model by reducing: mse for 1000 epochs, model number: 0\n",
            "\n",
            "Epoch 00591: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00707: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "\n",
            "Epoch 00807: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "Optimizing model by reducing: mape for 1000 epochs, model number: 0\n",
            "\n",
            "Epoch 00165: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00282: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "\n",
            "Epoch 00382: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "Optimizing model by reducing: mae for 1000 epochs, model number: 1\n",
            "Optimizing model by reducing: mse for 1000 epochs, model number: 1\n",
            "\n",
            "Epoch 00409: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00509: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Optimizing model by reducing: mape for 1000 epochs, model number: 1\n",
            "\n",
            "Epoch 00185: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00726: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "\n",
            "Epoch 00826: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "Optimizing model by reducing: mae for 1000 epochs, model number: 2\n",
            "Optimizing model by reducing: mse for 1000 epochs, model number: 2\n",
            "Optimizing model by reducing: mape for 1000 epochs, model number: 2\n",
            "\n",
            "Epoch 00241: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00341: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Optimizing model by reducing: mae for 1000 epochs, model number: 3\n",
            "\n",
            "Epoch 00572: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Optimizing model by reducing: mse for 1000 epochs, model number: 3\n",
            "\n",
            "Epoch 00304: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00607: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "\n",
            "Epoch 00707: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "Optimizing model by reducing: mape for 1000 epochs, model number: 3\n",
            "\n",
            "Epoch 00301: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00401: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Optimizing model by reducing: mae for 1000 epochs, model number: 4\n",
            "Optimizing model by reducing: mse for 1000 epochs, model number: 4\n",
            "\n",
            "Epoch 00640: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00740: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Optimizing model by reducing: mape for 1000 epochs, model number: 4\n",
            "\n",
            "Epoch 00132: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00609: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "\n",
            "Epoch 00709: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "CPU times: user 6min 24s, sys: 38.6 s, total: 7min 3s\n",
            "Wall time: 7min 58s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rerdvQAdFptN"
      },
      "source": [
        "Look at all of those models! \n",
        "\n",
        "How about we now write a function to use the list of trained ensemble models to make predictions and then return a list of predictions (one set of predictions per model)?\n",
        "\n",
        "### Making predictions with an ensemble model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45YkOBo38qxn"
      },
      "source": [
        "# Create a function which uses a list of trained models to make and return a list of predictions\n",
        "def make_ensemble_preds(ensemble_models, data):\n",
        "  ensemble_preds = []\n",
        "  for model in ensemble_models:\n",
        "    preds = model.predict(data) # make predictions with current ensemble model\n",
        "    ensemble_preds.append(preds)\n",
        "  return tf.constant(tf.squeeze(ensemble_preds))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAfSXEmU0IKK",
        "outputId": "7b5c2106-4fe9-4f7b-dc76-360175500636"
      },
      "source": [
        "# Create a list of ensemble predictions\n",
        "ensemble_preds = make_ensemble_preds(ensemble_models=ensemble_models,\n",
        "                                     data=test_dataset)\n",
        "ensemble_preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:5 out of the last 22 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fdcef255d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 22 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fdcef255d40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:6 out of the last 23 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fdc77fed9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 23 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fdc77fed9e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(15, 556), dtype=float32, numpy=\n",
              "array([[ 8805.756,  8773.019,  9028.609, ..., 50112.656, 49132.555,\n",
              "        46455.695],\n",
              "       [ 8764.092,  8740.744,  9051.838, ..., 49355.098, 48502.336,\n",
              "        45333.934],\n",
              "       [ 8732.57 ,  8719.407,  9093.386, ..., 49921.9  , 47992.15 ,\n",
              "        45316.45 ],\n",
              "       ...,\n",
              "       [ 8938.421,  8773.84 ,  9045.577, ..., 49488.133, 49741.4  ,\n",
              "        46536.25 ],\n",
              "       [ 8724.761,  8805.311,  9094.972, ..., 49553.086, 48492.86 ,\n",
              "        45084.266],\n",
              "       [ 8823.311,  8768.297,  9047.492, ..., 49759.902, 48090.945,\n",
              "        45874.336]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_i42FmH_5dC"
      },
      "source": [
        "Now we've got a set of ensemble predictions, we can evaluate them against the ground truth values.\n",
        "\n",
        "However, since we've trained 15 models, there's going to be 15 sets of predictions. Rather than comparing every set of predictions to the ground truth, let's take the median (you could also take the mean too but [the median is usually more robust than the mean](https://www.johndcook.com/blog/2009/03/06/student-t-distribution-mean-median/))."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W5aGqy_C9Aev",
        "outputId": "42bf5a3f-1056-4ec3-9c61-93a24c773be3"
      },
      "source": [
        "# Evaluate ensemble model(s) predictions\n",
        "ensemble_results = evaluate_preds(y_true=y_test,\n",
        "                                  y_pred=np.median(ensemble_preds, axis=0)) # take the median across all ensemble predictions\n",
        "ensemble_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 567.4423,\n",
              " 'mape': 2.5843322,\n",
              " 'mase': 0.996839,\n",
              " 'mse': 1144512.9,\n",
              " 'rmse': 1069.8191}"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3EyXGTQL6xV"
      },
      "source": [
        "Nice! Looks like the ensemble model is the best performing model on the MAE metric so far."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ARst6lYc_-mb"
      },
      "source": [
        "### Plotting the prediction intervals (uncertainty estimates) of our ensemble\n",
        "\n",
        "Right now all of our model's (prior to the ensemble model) are predicting single points. \n",
        "\n",
        "Meaning, given a set of `WINDOW_SIZE=7` values, the model will predict `HORIZION=1`.\n",
        "\n",
        "But what might be more helpful than a single value?\n",
        "\n",
        "Perhaps a range of values?\n",
        "\n",
        "For example, if a model is predicting the price of Bitcoin to be 50,000USD tomorrow, would it be helpful to know it's predicting the 50,000USD because it's predicting the price to be between 48,000 and 52,000USD? (note: \"$\" has been omitted from the previous sentence due to formatting issues)\n",
        "\n",
        "Knowing the range of values a model is predicting may help you make better decisions for your forecasts.\n",
        "\n",
        "You'd know that although the model is predicting 50,000USD (a **point prediction**, or single value in time), the value could actually be within the range 48,000USD to 52,000USD (of course, the value could also be *outside* of this range as well, but we'll get to that later).\n",
        "\n",
        "These kind of prediction ranges are called **prediction intervals** or **uncertainty estimates**. And they're often as important as the forecast itself.\n",
        "\n",
        "Why?\n",
        "\n",
        "Because **point predictions** are almost always going to be wrong. So having a range of values can help with decision making.\n",
        "\n",
        "> 📖 **Resource(s):** \n",
        "> * The steps we're about to take have been inspired by the Machine Learning Mastery blog post [*Prediction Intervals for Deep Learning Neural Networks*](https://machinelearningmastery.com/prediction-intervals-for-deep-learning-neural-networks/). Check out the post for more options to measure uncertainty with neural networks.\n",
        "> * For an example of uncertainty estimates being used in the wild, I'd also refer to Uber's [*Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber*](https://eng.uber.com/neural-networks-uncertainty-estimation/) blog post.\n",
        "\n",
        "![example of Uber's prediction intervals for daily completed trips in San Francisco](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-prediction-intervals-from-ubers-uncertainty-measures.png)\n",
        "*Example of how uncertainty estimates and predictions intervals can give an understanding of where point predictions (a single number) may not include all of useful information you'd like to know. For example, your model's point prediction for Uber trips on New Years Eve might be 100 (a made up number) but really, the prediction intervals are between 55 and 153 (both made up for the example). In this case, preparing 100 rides might end up being 53 short (it could even be more, like the point prediction, the prediction intervals are also estimates). The image comes from Uber's [blog post on uncertainty estimation in neural networks](https://eng.uber.com/neural-networks-uncertainty-estimation/).*\n",
        "\n",
        "One way of getting the 95% condfidnece prediction intervals for a deep learning model is the bootstrap method: \n",
        "1. Take the predictions from a number of randomly initialized models (we've got this thanks to our ensemble model)\n",
        "2. Measure the standard deviation of the predictions\n",
        "3. Multiply standard deviation by [1.96](https://en.wikipedia.org/wiki/1.96) (assuming the distribution is Gaussian, 95% of observations fall within 1.96 standard deviations of the mean, this is why we initialized our neural networks with a normal distribution)\n",
        "4. To get the prediction interval upper and lower bounds, add and subtract the value obtained in (3) to the mean/median of the predictions made in (1)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ys17iCd-UFv"
      },
      "source": [
        "# Find upper and lower bounds of ensemble predictions\n",
        "def get_upper_lower(preds): # 1. Take the predictions of multiple randomly initialized deep learning neural networks\n",
        "  \n",
        "  # 2. Measure the standard deviation of the predictions\n",
        "  std = tf.math.reduce_std(preds, axis=0)\n",
        "  \n",
        "  # 3. Multiply the standard deviation by 1.96\n",
        "  interval = 1.96 * std # https://en.wikipedia.org/wiki/1.96 \n",
        "\n",
        "  # 4. Get the prediction interval upper and lower bounds\n",
        "  preds_mean = tf.reduce_mean(preds, axis=0)\n",
        "  lower, upper = preds_mean - interval, preds_mean + interval\n",
        "  return lower, upper\n",
        "\n",
        "# Get the upper and lower bounds of the 95% \n",
        "lower, upper = get_upper_lower(preds=ensemble_preds)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rr9MJPuJMVMt"
      },
      "source": [
        "Wonderful, now we've got the upper and lower bounds for the the 95% prediction interval, let's plot them against our ensemble model's predictions.\n",
        "\n",
        "To do so, we can use our plotting function as well as the [`matplotlib.pyplot.fill_between()`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.fill_between.html) method to shade in the space between the upper and lower bounds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlSqQHFD_JYa",
        "outputId": "bb2c5701-f300-40b2-ff7f-e710e7253029"
      },
      "source": [
        "# Get the median values of our ensemble preds\n",
        "ensemble_median = np.median(ensemble_preds, axis=0)\n",
        "\n",
        "# Plot the median of our ensemble preds along with the prediction intervals (where the predictions fall between)\n",
        "offset=500\n",
        "plt.figure(figsize=(10, 7))\n",
        "plt.plot(X_test.index[offset:], y_test[offset:], \"g\", label=\"Test Data\")\n",
        "plt.plot(X_test.index[offset:], ensemble_median[offset:], \"k-\", label=\"Ensemble Median\")\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"BTC Price\")\n",
        "plt.fill_between(X_test.index[offset:], \n",
        "                 (lower)[offset:], \n",
        "                 (upper)[offset:], label=\"Prediction Intervals\")\n",
        "plt.legend(loc=\"upper left\", fontsize=14);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYP6BtmwDIms"
      },
      "source": [
        "We've just plotted:\n",
        "* The test data (the ground truth Bitcoin prices)\n",
        "* The median of the ensemble predictions \n",
        "* The 95% prediction intervals (assuming the data is Gaussian/normal, the model is saying that 95% of the time, predicted value should fall between this range)\n",
        "\n",
        "What can you tell about the ensemble model from the plot above?\n",
        "\n",
        "It looks like the ensemble predictions are lagging slightly behind the actual data.\n",
        "\n",
        "And the prediction intervals are fairly low throughout.\n",
        "\n",
        "The combination of lagging predictions as well as low prediction intervals indicates that our ensemble model may be **overfitting** the data, meaning it's basically replicating what a naïve model would do and just predicting the previous timestep value for the next value.\n",
        "\n",
        "This would explain why previous attempts to beat the naïve forecast have been futile.\n",
        "\n",
        "We can test this hypothesis of overfitting by creating a model to make predictions into the future and seeing what they look like.\n",
        "\n",
        "> 🔑 **Note:** Our prediction intervals assume that the data we're using come from a Gaussian/normal distribution (also called a bell curve), however, open systems rarely follow the Gaussian. We'll see this later on with the turkey problem 🦃. For further reading on this topic, I'd recommend reading [*The Black Swan* by Nassim Nicholas Taleb](https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_Improbable), especially Part 2 and Chapter 15."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7gsBYiNMk2E"
      },
      "source": [
        "## Aside: two types of uncertainty (coconut and subway)\n",
        "\n",
        "Inheritly, you know you cannot predict the future.\n",
        "\n",
        "That doesn't mean trying to isn't valuable.\n",
        "\n",
        "For many things, future predictions are helpful. Such as knowing the bus you're trying to catch to the library leaves at 10:08am. The time 10:08am is a **point prediction**, if the bus left at a random time every day, how helpful would it be?\n",
        "\n",
        "Just like saying the price of Bitcoin tomorrow will be 50,000USD is a point prediction.\n",
        "\n",
        "However, as we've discussed knowing a **prediction interval** or **uncertainty estimate** can be as helpful or even more helpful than a point prediction itself.\n",
        "\n",
        "Uncertainty estimates seek out to qualitatively and quantitatively answer the questions:\n",
        "* What can my model know? (with perfect data, what's possible to learn?)\n",
        "* What doesn't my model know? (what can a model never predict?)\n",
        "\n",
        "There are two types of uncertainty in machine learning you should be aware of:\n",
        "\n",
        "* **Aleatoric uncertainty** - this type of uncertainty cannot be reduced, it is also referred to as \"data\" or \"subway\" uncertainty.\n",
        "  * Let's say your train is scheduled to arrive at 10:08am but very rarely does it arrive at *exactly* 10:08am. You know it's usually a minute or two either side and perhaps up to 10-minutes late if traffic is bad. Even with all the data you could imagine, this level of uncertainty is still going to be present (much of it being noise).\n",
        "  * When we measured prediction intervals, we were measuring a form of subway uncertainty for Bitcoin price predictions (a little either side of the point prediction).\n",
        "\n",
        "* **Epistemic uncertainty** - this type of uncertainty can be reduced, it is also referred to as \"model\" or \"coconut\" uncertainty, it is very hard to calculate.\n",
        "  * The analogy for coconut uncertainty involves whether or not you'd get hit on the head by a coconut when going to a beach. \n",
        "    * If you were at a beach with coconuts trees, as you could imagine, this would be very hard to calculate. How often does a coconut fall of a tree? Where are you standing? \n",
        "    * But you could reduce this uncertainty to zero by going to a beach without coconuts (collect more data about your situation).\n",
        "  * Model uncertainty can be reduced by collecting more data samples/building a model to capture different parameters about the data you're modelling.\n",
        "\n",
        "The lines between these are blurred (one type of uncertainty can change forms into the other) and they can be confusing at first but are important to keep in mind for any kind of time series prediction.\n",
        "\n",
        "If you ignore the uncertanties, are you really going to get a reliable prediction?\n",
        "\n",
        "Perhaps another example might help.\n",
        "\n",
        "### Uncertainty in dating\n",
        "\n",
        "Let's say you're going on a First Date Feedback Radio Show to help improve your dating skills.\n",
        "\n",
        "Where you go on a blind first date with a girl (feel free to replace girl with your own preference) and the radio hosts record the date and then playback snippets of where you could've improved.\n",
        "\n",
        "And now let's add a twist. \n",
        "\n",
        "Last week your friend went on the same show. They told you about the girl they met and how the conversation went.\n",
        "\n",
        "Because you're now a machine learning engineer, you decide to build a machine learning model to help you with first date conversations.\n",
        "\n",
        "What levels of uncertainty do we have here? \n",
        "\n",
        "From an **aleatory uncertainty** (data) point of view, no matter how many conversations of first dates you collect, the conversation you end up having will likely be different to the rest (the best conversations have no subject and appear random).\n",
        "\n",
        "From an **epistemic uncertainty** (model) point of view, if the date is truly blind and both parties don't know who they're seeing until they meet in person, the epistemic uncertainty would be high. Because now you have no idea who the person you're going to meet is nor what you might talk about.\n",
        "\n",
        "However, the level of epistemic uncertainty would be reduced if your friend told about the girl they went on a date with last week on the show and it turns out you're going on a date with the same girl.\n",
        "\n",
        "But even though you know a little bit about the girl, your **aleatory uncertainty** (or subway uncertainty) is still high because you're not sure where the conversation will go.\n",
        "\n",
        "If you're wondering where above scenario came from, it happened to me this morning. Good timing right?\n",
        "\n",
        "### Learning more on uncertainty\n",
        "\n",
        "The field of quantifying uncertainty estimation in machine learning is a growing area of research.\n",
        "\n",
        "If you'd like to learn more I'd recommend the following.\n",
        "\n",
        "> 📖 **Resources:** Places to learn more about uncertainty in machine learning/forecasting:\n",
        "* 🎥 [MIT 6.S191: Evidential Deep Learning and Uncertainty](https://youtu.be/toTcf7tZK8c) \n",
        "* [Uncertainty quantification on Wikipedia](https://en.wikipedia.org/wiki/Uncertainty_quantification#Aleatoric_and_epistemic_uncertainty)\n",
        "* [*Why you should care about the Nate Silver vs. Nassim Taleb Twitter war*](https://towardsdatascience.com/why-you-should-care-about-the-nate-silver-vs-nassim-taleb-twitter-war-a581dce1f5fc) by Isaac Faber - a great insight into the role of uncertainty in the example of election prediction. \n",
        "* [*3 facts about time series forecasting that surprise experienced machine learning practitioners*](https://towardsdatascience.com/3-facts-about-time-series-forecasting-that-surprise-experienced-machine-learning-practitioners-69c18ee89387) by Skander Hannachi - fantastic outline of some of the main mistakes people make when building forecasting models, especially forgetting about uncertainty estimates.\n",
        "* [*Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber*](https://eng.uber.com/neural-networks-uncertainty-estimation/) - a discussion on techniques Uber used to engineer uncertainty estimates into their time sereis neural networks.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UW9oxH47EK-6"
      },
      "source": [
        "## Model 9: Train a model on the full historical data to make predictions into future\n",
        "\n",
        "What would a forecasting model be worth if we didn't use it to predict into the future?\n",
        "\n",
        "It's time we created a model which is able to make future predictions on the price of Bitcoin.\n",
        "\n",
        "To make predictions into the future, we'll train a model on the full dataset and then get to make predictions to some future horizon.\n",
        "\n",
        "Why use the full dataset?\n",
        "\n",
        "Previously, we split our data into training and test sets to evaluate how our model did on pseudo-future data (the test set).\n",
        "\n",
        "But since the goal of a forecasting model is to predict values into the actual-future, we won't be using a test set.\n",
        "\n",
        "> 🔑 **Note:** Forecasting models need to be retrained every time a forecast is made. Why? Because if Bitcoin prices are updated daily and you predict the price for tomorrow. Your model is only really valid for one day. When a new price comes out (e.g. the next day), you'll have to retrain your model to incorporate that new price to predict the next forecast.\n",
        "\n",
        "Let's get some data ready."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lseRHbOYseR",
        "outputId": "caa7777f-a8ef-4098-d026-26879b4119f5"
      },
      "source": [
        "bitcoin_prices_windowed.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>block_reward</th>\n",
              "      <th>Price+1</th>\n",
              "      <th>Price+2</th>\n",
              "      <th>Price+3</th>\n",
              "      <th>Price+4</th>\n",
              "      <th>Price+5</th>\n",
              "      <th>Price+6</th>\n",
              "      <th>Price+7</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-10-01</th>\n",
              "      <td>123.65499</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-02</th>\n",
              "      <td>125.45500</td>\n",
              "      <td>25</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-03</th>\n",
              "      <td>108.58483</td>\n",
              "      <td>25</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-04</th>\n",
              "      <td>118.67466</td>\n",
              "      <td>25</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-05</th>\n",
              "      <td>121.33866</td>\n",
              "      <td>25</td>\n",
              "      <td>118.67466</td>\n",
              "      <td>108.58483</td>\n",
              "      <td>125.45500</td>\n",
              "      <td>123.65499</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Price block_reward    Price+1  ...  Price+5  Price+6  Price+7\n",
              "Date                                           ...                           \n",
              "2013-10-01  123.65499           25        NaN  ...      NaN      NaN      NaN\n",
              "2013-10-02  125.45500           25  123.65499  ...      NaN      NaN      NaN\n",
              "2013-10-03  108.58483           25  125.45500  ...      NaN      NaN      NaN\n",
              "2013-10-04  118.67466           25  108.58483  ...      NaN      NaN      NaN\n",
              "2013-10-05  121.33866           25  118.67466  ...      NaN      NaN      NaN\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HZ9ObQaYyVf"
      },
      "source": [
        "# Train model on entire data to make prediction for the next day \n",
        "X_all = bitcoin_prices_windowed.drop([\"Price\", \"block_reward\"], axis=1).dropna().to_numpy() # only want prices, our future model can be a univariate model\n",
        "y_all = bitcoin_prices_windowed.dropna()[\"Price\"].to_numpy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6clQZWFCYPAW"
      },
      "source": [
        "Windows and labels ready! Let's turn them into performance optimized TensorFlow Datasets by:\n",
        "1. Turning `X_all` and `y_all` into tensor Datasets using [`tf.data.Dataset.from_tensor_slices()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices)\n",
        "2. Combining the features and labels into a Dataset tuple using [`tf.data.Dataset.zip()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#zip)\n",
        "3. Batch and prefetch the data using [`tf.data.Dataset.batch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch) and [`tf.data.Dataset.prefetch()`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch) respectively"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-eX9FBPZYkR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d66cdfbe-60d1-41ef-e6fe-902b559cbf9a"
      },
      "source": [
        "# 1. Turn X and y into tensor Datasets\n",
        "features_dataset_all = tf.data.Dataset.from_tensor_slices(X_all)\n",
        "labels_dataset_all = tf.data.Dataset.from_tensor_slices(y_all)\n",
        "\n",
        "# 2. Combine features & labels\n",
        "dataset_all = tf.data.Dataset.zip((features_dataset_all, labels_dataset_all))\n",
        "\n",
        "# 3. Batch and prefetch for optimal performance\n",
        "BATCH_SIZE = 1024 # taken from Appendix D in N-BEATS paper\n",
        "dataset_all = dataset_all.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)\n",
        "\n",
        "dataset_all"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PrefetchDataset shapes: ((None, 7), (None,)), types: (tf.float64, tf.float64)>"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63wxSbcuY8RI"
      },
      "source": [
        "And now let's create a model similar to `model_1` except with an extra layer, we'll also fit it to the entire dataset for 100 epochs (feel free to play around with the number of epochs or callbacks here, you've got the skills to now)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCtmpaCGZ_fj",
        "outputId": "b81ba08b-8d8d-4305-f711-4d18d2aff51f"
      },
      "source": [
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create model (nice and simple, just to test)\n",
        "model_9 = tf.keras.Sequential([\n",
        "  layers.Dense(128, activation=\"relu\"),\n",
        "  layers.Dense(128, activation=\"relu\"),\n",
        "  layers.Dense(HORIZON)\n",
        "])\n",
        "\n",
        "# Compile\n",
        "model_9.compile(loss=tf.keras.losses.mae,\n",
        "                optimizer=tf.keras.optimizers.Adam())\n",
        "\n",
        "# Fit model on all of the data to make future forecasts\n",
        "model_9.fit(dataset_all,\n",
        "            epochs=100,\n",
        "            verbose=0) # don't print out anything, we've seen this all before"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdc77ae0dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dv1Z7Q8N4qI5"
      },
      "source": [
        "### Make predictions on the future\n",
        "\n",
        "Let's predict the future and get rich!\n",
        "\n",
        "Well... maybe not.\n",
        "\n",
        "As you've seen so far, our machine learning models have performed quite poorly at predicting the price of Bitcoin (time series forecasting in open systems is typically a game of luck), often worse than the naive forecast.\n",
        "\n",
        "That doesn't mean we can't use our models to *try* and predict into the future right?\n",
        "\n",
        "To do so, let's start by defining a variable `INTO_FUTURE` which decides how many timesteps we'd like to predict into the future."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYxHD1qPA1Mj"
      },
      "source": [
        "# How many timesteps to predict into the future?\n",
        "INTO_FUTURE = 14 # since our Bitcoin data is daily, this is for 14 days"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4b2Flb1Wb4G4"
      },
      "source": [
        "![flowchart representation prediction loop to make forecasts and then append forecasts to data and make more forecasts continuously until forecast horizon is exhausted](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-prediction-loop-for-forecasts.png)\n",
        "*Example flow chart representing the loop we're about to create for making forecasts. Not pictured: retraining a forecasting model every time a forecast is made & new data is acquired. For example, if you're predicting the price of Bitcoin daily, you'd want to retrain your model every day, since each day you're going to have a new data point to work with.*\n",
        "\n",
        "Alright, let's create a function which returns `INTO_FUTURE` forecasted values using a trained model.\n",
        "\n",
        "To do so, we'll build the following steps:\n",
        "1. Function which takes as input: \n",
        "  * a list of values (the Bitcoin historical data)\n",
        "  * a trained model (such as `model_9`)\n",
        "  * a window into the future to predict (our `INTO_FUTURE` variable)\n",
        "  * the window size a model was trained on (`WINDOW_SIZE`) - the model can only predict on the same kind of data it was trained on\n",
        "2. Creates an empty list for future forecasts (this will be returned at the end of the function) and extracts the last `WINDOW_SIZE` values from the input values (predictions will start from the last `WINDOW_SIZE` values of the training data)\n",
        "3. Loop `INTO_FUTURE` times making a prediction on `WINDOW_SIZE` datasets which update to remove the first the value and append the latest prediction \n",
        "  * Eventually future predictions will be made using the model's own previous predictions as input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEtTHMV_iE7f"
      },
      "source": [
        "# 1. Create function to make predictions into the future\n",
        "def make_future_forecast(values, model, into_future, window_size=WINDOW_SIZE) -> list:\n",
        "  \"\"\"\n",
        "  Makes future forecasts into_future steps after values ends.\n",
        "\n",
        "  Returns future forecasts as list of floats.\n",
        "  \"\"\"\n",
        "  # 2. Make an empty list for future forecasts/prepare data to forecast on\n",
        "  future_forecast = []\n",
        "  last_window = values[-WINDOW_SIZE:] # only want preds from the last window (this will get updated)\n",
        "\n",
        "  # 3. Make INTO_FUTURE number of predictions, altering the data which gets predicted on each time \n",
        "  for _ in range(into_future):\n",
        "    \n",
        "    # Predict on last window then append it again, again, again (model starts to make forecasts on its own forecasts)\n",
        "    future_pred = model.predict(tf.expand_dims(last_window, axis=0))\n",
        "    print(f\"Predicting on: \\n {last_window} -> Prediction: {tf.squeeze(future_pred).numpy()}\\n\")\n",
        "    \n",
        "    # Append predictions to future_forecast\n",
        "    future_forecast.append(tf.squeeze(future_pred).numpy())\n",
        "    # print(future_forecast)\n",
        "\n",
        "    # Update last window with new pred and get WINDOW_SIZE most recent preds (model was trained on WINDOW_SIZE windows)\n",
        "    last_window = np.append(last_window, future_pred)[-WINDOW_SIZE:]\n",
        "  \n",
        "  return future_forecast"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shcu6e7EeSWz"
      },
      "source": [
        "Nice! Time to bring BitPredict 💰📈 to life and make future forecasts of the price of Bitcoin.\n",
        "\n",
        "> 🛠 **Exercise:** In terms of a forecasting model, what might another approach to our `make_future_forecasts()` function? Recall, that for making forecasts, you need to retrain a model each time you want to generate a new prediction. \n",
        ">\n",
        "> So perhaps you could try to: make a prediction (one timestep into the future), retrain a model with this new prediction appended to the data, make a prediction, append the prediction, retrain a model... etc. \n",
        ">\n",
        "> As it is, the `make_future_forecasts()` function skips the retraining of a model part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZVNYdqJ5zo9",
        "outputId": "348d457b-3ab1-4b99-bd99-96e643c8c749"
      },
      "source": [
        "# Make forecasts into future of the price of Bitcoin\n",
        "# Note: if you're reading this at a later date, you may already be in the future, so the forecasts \n",
        "# we're making may not actually be forecasts, if that's the case, readjust the training data.\n",
        "future_forecast = make_future_forecast(values=y_all,\n",
        "                                       model=model_9,\n",
        "                                       into_future=INTO_FUTURE,\n",
        "                                       window_size=WINDOW_SIZE)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicting on: \n",
            " [56573.5554719  52147.82118698 49764.1320816  50032.69313676\n",
            " 47885.62525472 45604.61575361 43144.47129086] -> Prediction: 55764.46484375\n",
            "\n",
            "Predicting on: \n",
            " [52147.82118698 49764.1320816  50032.69313676 47885.62525472\n",
            " 45604.61575361 43144.47129086 55764.46484375] -> Prediction: 50985.9453125\n",
            "\n",
            "Predicting on: \n",
            " [49764.1320816  50032.69313676 47885.62525472 45604.61575361\n",
            " 43144.47129086 55764.46484375 50985.9453125 ] -> Prediction: 48522.96484375\n",
            "\n",
            "Predicting on: \n",
            " [50032.69313676 47885.62525472 45604.61575361 43144.47129086\n",
            " 55764.46484375 50985.9453125  48522.96484375] -> Prediction: 48137.203125\n",
            "\n",
            "Predicting on: \n",
            " [47885.62525472 45604.61575361 43144.47129086 55764.46484375\n",
            " 50985.9453125  48522.96484375 48137.203125  ] -> Prediction: 47880.63671875\n",
            "\n",
            "Predicting on: \n",
            " [45604.61575361 43144.47129086 55764.46484375 50985.9453125\n",
            " 48522.96484375 48137.203125   47880.63671875] -> Prediction: 46879.71875\n",
            "\n",
            "Predicting on: \n",
            " [43144.47129086 55764.46484375 50985.9453125  48522.96484375\n",
            " 48137.203125   47880.63671875 46879.71875   ] -> Prediction: 48227.6015625\n",
            "\n",
            "Predicting on: \n",
            " [55764.46484375 50985.9453125  48522.96484375 48137.203125\n",
            " 47880.63671875 46879.71875    48227.6015625 ] -> Prediction: 53963.69140625\n",
            "\n",
            "Predicting on: \n",
            " [50985.9453125  48522.96484375 48137.203125   47880.63671875\n",
            " 46879.71875    48227.6015625  53963.69140625] -> Prediction: 49685.55859375\n",
            "\n",
            "Predicting on: \n",
            " [48522.96484375 48137.203125   47880.63671875 46879.71875\n",
            " 48227.6015625  53963.69140625 49685.55859375] -> Prediction: 47596.17578125\n",
            "\n",
            "Predicting on: \n",
            " [48137.203125   47880.63671875 46879.71875    48227.6015625\n",
            " 53963.69140625 49685.55859375 47596.17578125] -> Prediction: 48114.4296875\n",
            "\n",
            "Predicting on: \n",
            " [47880.63671875 46879.71875    48227.6015625  53963.69140625\n",
            " 49685.55859375 47596.17578125 48114.4296875 ] -> Prediction: 48808.0078125\n",
            "\n",
            "Predicting on: \n",
            " [46879.71875    48227.6015625  53963.69140625 49685.55859375\n",
            " 47596.17578125 48114.4296875  48808.0078125 ] -> Prediction: 48623.85546875\n",
            "\n",
            "Predicting on: \n",
            " [48227.6015625  53963.69140625 49685.55859375 47596.17578125\n",
            " 48114.4296875  48808.0078125  48623.85546875] -> Prediction: 50178.72265625\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFZmkWRccmQ5",
        "outputId": "c33733d2-cc17-4a92-9f87-db55c5494d14"
      },
      "source": [
        "future_forecast[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[55764.465,\n",
              " 50985.945,\n",
              " 48522.965,\n",
              " 48137.203,\n",
              " 47880.637,\n",
              " 46879.72,\n",
              " 48227.6,\n",
              " 53963.69,\n",
              " 49685.56,\n",
              " 47596.176]"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuaY0cOaCHfr"
      },
      "source": [
        "### Plot future forecasts\n",
        "\n",
        "This is so exciting! Forecasts made! \n",
        "\n",
        "But right now, they're just numbers on a page.\n",
        "\n",
        "Let's bring them to life by adhering to the data explorer's motto: visualize, visualize, visualize!\n",
        "\n",
        "To plot our model's future forecasts against the historical data of Bitcoin, we're going to need a series of future dates (future dates from the final date of where our dataset ends).\n",
        "\n",
        "How about we create a function to return a date range from some specified start date to a specified number of days into the future (`INTO_FUTURE`).\n",
        "\n",
        "To do so, we'll use a combination of NumPy's [`datetime64` datatype](https://numpy.org/doc/stable/reference/arrays.datetime.html) (our Bitcoin dates are already in this datatype) as well as NumPy's `timedelta64` method which helps to create date ranges."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4iNNliSCbU-"
      },
      "source": [
        "def get_future_dates(start_date, into_future, offset=1):\n",
        "  \"\"\"\n",
        "  Returns array of datetime values from ranging from start_date to start_date+horizon.\n",
        "\n",
        "  start_date: date to start range (np.datetime64)\n",
        "  into_future: number of days to add onto start date for range (int)\n",
        "  offset: number of days to offset start_date by (default 1)\n",
        "  \"\"\"\n",
        "  start_date = start_date + np.timedelta64(offset, \"D\") # specify start date, \"D\" stands for day\n",
        "  end_date = start_date + np.timedelta64(into_future, \"D\") # specify end date\n",
        "  return np.arange(start_date, end_date, dtype=\"datetime64[D]\") # return a date range between start date and end date"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sob2VY8chLA5"
      },
      "source": [
        "The start date of our forecasted dates will be the last date of our dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6676Sl-cAFn5",
        "outputId": "fbfc545a-a313-4866-ea98-c4f1b397d0bf"
      },
      "source": [
        "# Last timestep of timesteps (currently in np.datetime64 format)\n",
        "last_timestep = bitcoin_prices.index[-1]\n",
        "last_timestep"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2021-05-18 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "252bhVD7C5NZ",
        "outputId": "17065a39-4de8-4528-a380-2415a528db8c"
      },
      "source": [
        "# Get next two weeks of timesteps\n",
        "next_time_steps = get_future_dates(start_date=last_timestep, \n",
        "                                   into_future=INTO_FUTURE)\n",
        "next_time_steps"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2021-05-19', '2021-05-20', '2021-05-21', '2021-05-22',\n",
              "       '2021-05-23', '2021-05-24', '2021-05-25', '2021-05-26',\n",
              "       '2021-05-27', '2021-05-28', '2021-05-29', '2021-05-30',\n",
              "       '2021-05-31', '2021-06-01'], dtype='datetime64[D]')"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5FIVsQJhVVs"
      },
      "source": [
        "Look at that! We've now got a list of dates we can use to visualize our future Bitcoin predictions.\n",
        "\n",
        "But to make sure the lines of the plot connect (try not running the cell below and then plotting the data to see what I mean), let's insert the last timestep and Bitcoin price of our training data to the `next_time_steps` and `future_forecast` arrays."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLXAudg3hIo9",
        "outputId": "c33b3c8d-8fd5-49c3-951e-d111d6b6c5e8"
      },
      "source": [
        "# Insert last timestep/final price so the graph doesn't look messed\n",
        "next_time_steps = np.insert(next_time_steps, 0, last_timestep)\n",
        "future_forecast = np.insert(future_forecast, 0, btc_price[-1])\n",
        "next_time_steps, future_forecast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['2021-05-18', '2021-05-19', '2021-05-20', '2021-05-21',\n",
              "        '2021-05-22', '2021-05-23', '2021-05-24', '2021-05-25',\n",
              "        '2021-05-26', '2021-05-27', '2021-05-28', '2021-05-29',\n",
              "        '2021-05-30', '2021-05-31', '2021-06-01'], dtype='datetime64[D]'),\n",
              " array([43144.473, 55764.465, 50985.945, 48522.965, 48137.203, 47880.637,\n",
              "        46879.72 , 48227.6  , 53963.69 , 49685.56 , 47596.176, 48114.43 ,\n",
              "        48808.008, 48623.855, 50178.723], dtype=float32))"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xDtk3wHhyDQ"
      },
      "source": [
        "Time to plot!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "LUo-rjmpfuM0",
        "outputId": "79e101a7-7475-49fe-a1ee-1532578a3a80"
      },
      "source": [
        "# Plot future price predictions of Bitcoin\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_time_series(bitcoin_prices.index, btc_price, start=2500, format=\"-\", label=\"Actual BTC Price\")\n",
        "plot_time_series(next_time_steps, future_forecast, format=\"-\", label=\"Predicted BTC Price\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnNT5pK4h_4X"
      },
      "source": [
        "Hmmm... how did our model go? \n",
        "\n",
        "It looks like our predictions are starting to form a bit of a cyclic pattern (up and down in the same way).\n",
        "\n",
        "Perhaps that's due to our model overfitting the training data and not generalizing well for future data. Also, as you could imagine, the further you predict into the future, the higher your chance for error (try seeing what happens when you predict 100 days into the future).\n",
        "\n",
        "But of course, we can't measure these predictions as they are because after all, they're predictions into the actual-future (by the time you read this, the future might have already happened, if so, how did the model go?).\n",
        "\n",
        "> 🔑 **Note:** A reminder, the predictions we've made here are not financial advice. And by now, you should be well aware of just how poor machine learning models can be at forecasting values in an open system - anyone promising you a model which can \"beat the market\" is likely trying to scam you, oblivious to their errors or very lucky.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5VMaZ1UBpXt"
      },
      "source": [
        "## Model 10: Why forecasting is BS (the turkey problem 🦃)\n",
        "\n",
        "When creating any kind of forecast, you must keep the **turkey problem** in mind.\n",
        "\n",
        "The **turkey problem** is an analogy for when your observational data (your historical data) fails to capture a future event which is catostrophic and could lead you to ruin.\n",
        "\n",
        "The story goes, a turkey lives a good life for 1000 days, being fed every day and taken care of by its owners until the evening before Thanksgiving.\n",
        "\n",
        "Based on the turkey's observational data, it has no reason to believe things shouldn't keep going the way they are.\n",
        "\n",
        "In other words, how could a turkey possibly predict that on day 1001, after 1000 consectutive good days, it was about to have a far from ideal day.\n",
        "\n",
        "![the turkey problem illustrated, a turkey lives 1000 good days until the eve of thanksgiving](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-the-turkey-problem.png)\n",
        "*Example of the turkey problem. A turkey might live 1000 good days and none of them would be a sign of what's to happen on day 1001. Similar with forecasting, your historical data may not have any indication of a change which is about to come. The graph image is from page 41 of [The Black Swan](https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_Improbable) by Nassim Taleb (I added in the turkey graphics).*\n",
        "\n",
        "How does this relate to predicting the price of Bitcoin (or the price of any stock or figure in an open market)?\n",
        "\n",
        "You could have the historical data of Bitcoin for its entire existence and build a model which predicts it perfectly.\n",
        "\n",
        "But then one day for some unknown and unpredictable reason, the price of Bitcoin plummets 100x in a single day.\n",
        "\n",
        "Of course, this kind of scenario is unlikely.\n",
        "\n",
        "But that doesn't take away from its significance.\n",
        "\n",
        "Think about it in your own life, how many times have the most significant events happened seemingly out of the blue?\n",
        "\n",
        "As in, you could go to a cafe and run into the love of your life, despite visiting the same cafe for 10-years straight and never running into this person before. \n",
        "\n",
        "The same thing goes for predicting the price of Bitcoin, you could make money for 10-years straight and then lose it all in a single day.\n",
        "\n",
        "It doesn't matter how many times you get paid, it matters the amount you get paid.\n",
        "\n",
        "> 📖 **Resource:** If you'd like to learn more about the turkey problem, I'd recommend the following:\n",
        "* [Explaining both the XIV trade and why forecasting is BS](https://youtu.be/hRwimmE2wEk) by Nassim Taleb\n",
        "* [*The Black Swan*](https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_Improbable) by Nassim Taleb (epsecially Chapter 4 which outlines and discusses the turkey problem)\n",
        "\n",
        "Let's get specific and see how the turkey problem effects us modelling the historical and future price of Bitcoin.\n",
        "\n",
        "To do so, we're going to manufacture a highly unlikely data point into the historical price of Bitcoin, the price falling 100x in one day.\n",
        "\n",
        "> 🔑 **Note:** A very unlikely and unpredictable event such as the price of Bitcoin falling 100x in a single day (note: the adjective \"unlikely\" is based on the historical price changes of Bitcoin) is also referred to a [**Black Swan** event](https://en.wikipedia.org/wiki/Black_swan_theory). A Black Swan event is an unknown unknown, you have no way of predicting whether or not it will happen but these kind of events often have a large impact. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KD50Xt36BsOk"
      },
      "source": [
        "# Let's introduce a Turkey problem to our BTC data (price BTC falls 100x in one day)\n",
        "btc_price_turkey = btc_price.copy()\n",
        "btc_price_turkey[-1] = btc_price_turkey[-1] / 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUFWcnNGB7yn",
        "outputId": "e91d1f3c-e68f-4a53-c22e-2b56db90cd1e"
      },
      "source": [
        "# Manufacture an extra price on the end (to showcase the Turkey problem)\n",
        "btc_price_turkey[-10:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[58788.2096789273,\n",
              " 58102.1914262342,\n",
              " 55715.5466512869,\n",
              " 56573.5554719043,\n",
              " 52147.8211869823,\n",
              " 49764.1320815975,\n",
              " 50032.6931367648,\n",
              " 47885.6252547166,\n",
              " 45604.6157536131,\n",
              " 431.44471290860304]"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53W9vjWutwof"
      },
      "source": [
        "Notice the last value is 100x lower than what it actually was (remember, this is not a real data point, its only to illustrate the effects of the turkey problem).\n",
        "\n",
        "Now we've got Bitcoin prices including a turkey problem data point, let's get the timesteps. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqwwcjGZ50Ji",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1084edb-9668-4ac1-c2e3-64a6bee52eda"
      },
      "source": [
        "# Get the timesteps for the turkey problem \n",
        "btc_timesteps_turkey = np.array(bitcoin_prices.index)\n",
        "btc_timesteps_turkey[-10:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2021-05-09T00:00:00.000000000', '2021-05-10T00:00:00.000000000',\n",
              "       '2021-05-11T00:00:00.000000000', '2021-05-12T00:00:00.000000000',\n",
              "       '2021-05-13T00:00:00.000000000', '2021-05-14T00:00:00.000000000',\n",
              "       '2021-05-15T00:00:00.000000000', '2021-05-16T00:00:00.000000000',\n",
              "       '2021-05-17T00:00:00.000000000', '2021-05-18T00:00:00.000000000'],\n",
              "      dtype='datetime64[ns]')"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sr26An3zuHaV"
      },
      "source": [
        "Beautiful! Let's see our artificially created turkey problem Bitcoin data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "DgG2hk9PB7wi",
        "outputId": "a2ef43b9-07b7-43eb-f793-019e9e629548"
      },
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "plot_time_series(timesteps=btc_timesteps_turkey, \n",
        "                 values=btc_price_turkey, \n",
        "                 format=\"-\", \n",
        "                 label=\"BTC Price + Turkey Problem\", \n",
        "                 start=2500)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpssnGvyuRkM"
      },
      "source": [
        "How do you think building a model on this data will go?\n",
        "\n",
        "Remember, all we've changed is a single data point out of our entire dataset.\n",
        "\n",
        "Before we build a model, let's create some windowed datasets with our turkey data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8BjMZYk7E9f",
        "outputId": "d3389130-a03d-4511-ce4d-97a1806d0bbf"
      },
      "source": [
        "# Create train and test sets for turkey problem data\n",
        "full_windows, full_labels = make_windows(np.array(btc_price_turkey), window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(full_windows), len(full_labels)\n",
        "\n",
        "X_train, X_test, y_train, y_test = make_train_test_splits(full_windows, full_labels)\n",
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2224, 556, 2224, 556)"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JJVipEl7884"
      },
      "source": [
        "### Building a turkey model (model to predict on turkey data)\n",
        "\n",
        "With our updated data, we only changed 1 value.\n",
        "\n",
        "Let's see how it effects a model.\n",
        "\n",
        "To keep things comparable to previous models, we'll create a `turkey_model` which is a clone of `model_1` (same architecture, but different data).\n",
        "\n",
        "That way, when we evaluate the `turkey_model` we can compare its results to `model_1_results` and see how much a single data point can influence a model's performance.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WbuRdsPn69KI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18dcee61-6c2c-4f2b-8a7f-05a8c6e96194"
      },
      "source": [
        "# Clone model 1 architecture for turkey model and fit the turkey model on the turkey data\n",
        "turkey_model = tf.keras.models.clone_model(model_1)\n",
        "turkey_model._name = \"Turkey_Model\"\n",
        "turkey_model.compile(loss=\"mae\",\n",
        "                     optimizer=tf.keras.optimizers.Adam())\n",
        "turkey_model.fit(X_train, y_train,\n",
        "                 epochs=100,\n",
        "                 verbose=0,\n",
        "                 validation_data=(X_test, y_test),\n",
        "                 callbacks=[create_model_checkpoint(turkey_model.name)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: model_experiments/Turkey_Model/assets\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdc7a4dd550>"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMGvTvSK9e0S",
        "outputId": "19e14aa6-1681-45c1-d4fa-ca6968dd37f5"
      },
      "source": [
        "# Evaluate turkey model on test data\n",
        "turkey_model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 696.1285\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "696.1284790039062"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLSwuKgzB7q_",
        "outputId": "9c25bf4a-611a-422b-e281-89b05f88db7d"
      },
      "source": [
        "# Load best model and evaluate on test data\n",
        "turkey_model = tf.keras.models.load_model(\"model_experiments/Turkey_Model/\")\n",
        "turkey_model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 2ms/step - loss: 638.3047\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "638.3046875"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfe3OIGEvoLi"
      },
      "source": [
        "Alright, now let's make some predictions with our model and evaluate them on the test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2pmQp2w9wyV",
        "outputId": "3ac4f12b-7b69-469a-adb4-fdf393274caf"
      },
      "source": [
        "# Make predictions with Turkey model\n",
        "turkey_preds = make_preds(turkey_model, X_test)\n",
        "turkey_preds[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              "array([8858.391, 8803.98 , 9039.575, 8785.937, 8778.044, 8735.638,\n",
              "       8684.118, 8558.659, 8461.373, 8542.206], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sTeBtiO9-fk",
        "outputId": "e32d4f72-bff8-4d86-cc08-47e5ff8b2324"
      },
      "source": [
        "# Evaluate turkey preds\n",
        "turkey_results = evaluate_preds(y_true=y_test,\n",
        "                                y_pred=turkey_preds)\n",
        "turkey_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 17144.766,\n",
              " 'mape': 121.58286,\n",
              " 'mase': 26.53158,\n",
              " 'mse': 615487800.0,\n",
              " 'rmse': 23743.305}"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hW3hjB1a-Rji"
      },
      "source": [
        "And with just one value change, our error metrics go through the roof.\n",
        "\n",
        "To make sure, let's remind ourselves of how `model_1` went on unmodified Bitcoin data (no turkey problem)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSScSrh5wxjV",
        "outputId": "8622fbc7-1348-4e69-9d60-0779a608672c"
      },
      "source": [
        "model_1_results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 568.95123,\n",
              " 'mape': 2.5448983,\n",
              " 'mase': 0.9994897,\n",
              " 'mse': 1171744.0,\n",
              " 'rmse': 1082.4713}"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iav7HuGBw1En"
      },
      "source": [
        "By changing just one value, the `turkey_model` MAE increases almost 30x over `model_1`.\n",
        "\n",
        "Finally, we'll visualize the turkey predictions over the test turkey data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pDA7sWVx-Y4p",
        "outputId": "4d2386ad-621c-4678-9cbc-25b5c6f174dd"
      },
      "source": [
        "plt.figure(figsize=(10, 7))\n",
        "# plot_time_series(timesteps=btc_timesteps_turkey[:split_size], values=btc_price_turkey[:split_size], label=\"Train Data\")\n",
        "offset=300\n",
        "plot_time_series(timesteps=btc_timesteps_turkey[-len(X_test):], \n",
        "                 values=btc_price_turkey[-len(y_test):], \n",
        "                 format=\"-\", \n",
        "                 label=\"Turkey Test Data\", start=offset)\n",
        "plot_time_series(timesteps=btc_timesteps_turkey[-len(X_test):],\n",
        "                 values=turkey_preds, \n",
        "                 label=\"Turkey Preds\", \n",
        "                 start=offset);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRRXsMpMtPfO"
      },
      "source": [
        "Why does this happen?\n",
        "\n",
        "Why does our model fail to capture the turkey problem data point?\n",
        "\n",
        "Think about it like this, just like a turkey who lives 1000 joyful days, based on observation alone has no reason to believe day 1001 won't be as joyful as the last, a model which has been trained on historical data of Bitcoin which has no single event where the price decreased by 100x in a day, has no reason to predict it will in the future.\n",
        "\n",
        "A model cannot predict anything in the future outside of the distribution it was trained on.\n",
        "\n",
        "In turn, highly unlikely price movements (based on historical movements), upward or downward will likely never be part of a forecast.\n",
        "\n",
        "However, as we've seen, despite their unlikeliness, these events can have huuuuuuuuge impacts to the performance of our models.\n",
        "\n",
        "> 📖 **Resource:** For a great article which discusses Black Swan events and how they often get ignored due to the assumption that historical events come from a certain distribution and that future events will come from the same distribution see [*Black Swans, Normal Distributions and Supply Chain Risk*](https://spendmatters.com/uk/black-swans-normal-distributions-supply-chain-risk/) by Spend Matters. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWwHor7lBR3b"
      },
      "source": [
        "## Compare Models\n",
        "\n",
        "We've trained a bunch of models.\n",
        "\n",
        "And if anything, we've seen just how poorly machine learning and deep learning models are at forecasting the price of Bitcoin (or any kind of open market value).\n",
        "\n",
        "To highlight this, let's compare the results of all of the modelling experiments we've performed so far."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "vZtkauQXBXVx",
        "outputId": "af622e83-258b-4d7a-de18-5e59027b169d"
      },
      "source": [
        "# Compare different model results (w = window, h = horizon, e.g. w=7 means a window size of 7)\n",
        "model_results = pd.DataFrame({\"naive_model\": naive_results,\n",
        "                              \"model_1_dense_w7_h1\": model_1_results,\n",
        "                              \"model_2_dense_w30_h1\": model_2_results,\n",
        "                              \"model_3_dense_w30_h7\": model_3_results,\n",
        "                              \"model_4_CONV1D\": model_4_results,\n",
        "                              \"model_5_LSTM\": model_5_results,\n",
        "                              \"model_6_multivariate\": model_6_results,\n",
        "                              \"model_8_NBEATs\": model_7_results,\n",
        "                              \"model_9_ensemble\": ensemble_results,\n",
        "                              \"model_10_turkey\": turkey_results}).T\n",
        "model_results.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mae</th>\n",
              "      <th>mse</th>\n",
              "      <th>rmse</th>\n",
              "      <th>mape</th>\n",
              "      <th>mase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>naive_model</th>\n",
              "      <td>567.980225</td>\n",
              "      <td>1.147547e+06</td>\n",
              "      <td>1071.236206</td>\n",
              "      <td>2.516525</td>\n",
              "      <td>0.999570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_1_dense_w7_h1</th>\n",
              "      <td>568.951233</td>\n",
              "      <td>1.171744e+06</td>\n",
              "      <td>1082.471313</td>\n",
              "      <td>2.544898</td>\n",
              "      <td>0.999490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_2_dense_w30_h1</th>\n",
              "      <td>608.961487</td>\n",
              "      <td>1.281439e+06</td>\n",
              "      <td>1132.006470</td>\n",
              "      <td>2.769339</td>\n",
              "      <td>1.064471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_3_dense_w30_h7</th>\n",
              "      <td>1237.506348</td>\n",
              "      <td>5.405198e+06</td>\n",
              "      <td>1425.747681</td>\n",
              "      <td>5.558878</td>\n",
              "      <td>2.202074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_4_CONV1D</th>\n",
              "      <td>570.828308</td>\n",
              "      <td>1.176671e+06</td>\n",
              "      <td>1084.744751</td>\n",
              "      <td>2.559336</td>\n",
              "      <td>1.002787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_5_LSTM</th>\n",
              "      <td>596.644653</td>\n",
              "      <td>1.273487e+06</td>\n",
              "      <td>1128.488770</td>\n",
              "      <td>2.683845</td>\n",
              "      <td>1.048139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_6_multivariate</th>\n",
              "      <td>567.587402</td>\n",
              "      <td>1.161688e+06</td>\n",
              "      <td>1077.816528</td>\n",
              "      <td>2.541387</td>\n",
              "      <td>0.997094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_8_NBEATs</th>\n",
              "      <td>585.499817</td>\n",
              "      <td>1.179492e+06</td>\n",
              "      <td>1086.043945</td>\n",
              "      <td>2.744519</td>\n",
              "      <td>1.028561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_9_ensemble</th>\n",
              "      <td>567.442322</td>\n",
              "      <td>1.144513e+06</td>\n",
              "      <td>1069.819092</td>\n",
              "      <td>2.584332</td>\n",
              "      <td>0.996839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_10_turkey</th>\n",
              "      <td>17144.765625</td>\n",
              "      <td>6.154878e+08</td>\n",
              "      <td>23743.304688</td>\n",
              "      <td>121.582863</td>\n",
              "      <td>26.531580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               mae           mse  ...        mape       mase\n",
              "naive_model             567.980225  1.147547e+06  ...    2.516525   0.999570\n",
              "model_1_dense_w7_h1     568.951233  1.171744e+06  ...    2.544898   0.999490\n",
              "model_2_dense_w30_h1    608.961487  1.281439e+06  ...    2.769339   1.064471\n",
              "model_3_dense_w30_h7   1237.506348  5.405198e+06  ...    5.558878   2.202074\n",
              "model_4_CONV1D          570.828308  1.176671e+06  ...    2.559336   1.002787\n",
              "model_5_LSTM            596.644653  1.273487e+06  ...    2.683845   1.048139\n",
              "model_6_multivariate    567.587402  1.161688e+06  ...    2.541387   0.997094\n",
              "model_8_NBEATs          585.499817  1.179492e+06  ...    2.744519   1.028561\n",
              "model_9_ensemble        567.442322  1.144513e+06  ...    2.584332   0.996839\n",
              "model_10_turkey       17144.765625  6.154878e+08  ...  121.582863  26.531580\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "PrLcCM-LFv2B",
        "outputId": "c96cae30-d0dd-4c1d-eaf3-4b4258e96639"
      },
      "source": [
        "# Sort model results by MAE and plot them\n",
        "model_results[[\"mae\"]].sort_values(by=\"mae\").plot(figsize=(10, 7), kind=\"bar\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDW1xGEtyzyRnTn1dm+T3k/xpksum2p8w9ZyXJ7k4yYVJDphqP7BvuzjJEYs9KUmSpKHYbkufWFUXAvsAJNkWuAz4KPBs4K+q6s3Tj09yb+Bg4D7AnYDPJrlHf/hvgMcC64HTkpxQVedtaW2SJElDscVhbI79gW9U1aVJNvaYg4D3V9VPgG8muRh4cH/s4qq6BCDJ+/vHGsYkSdLozWrM2MHA+6buvzjJWUmOTbJT37Yr8O2px6zv2zbWfhNJDk+yNsnaK6+8ckalS5IktbPoMJbkZsCTgA/1TUcBd6O7hPkd4C2L/R0TVXV0Va2pqjWrVq2a1Y+VJElqZhaXKR8PfK2qLgeYfAdIcgzwyf7uZcCdp563um9jgXZJkqRRm8VlykOYukSZ5I5Tx54CnNPfPgE4OMnNk+wB7AmcCpwG7Jlkj76X7eD+sZIkSaO3qJ6xJLeimwX5/KnmP0+yD1DAusmxqjo3yQfpBuZfD7yoqm7of86Lgc8A2wLHVtW5i6lLkiRpKBYVxqrqh8Ad5rT91gKPfx3wunnaPwV8ajG1SJIkDZEr8EuSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqaFFh7Ek65KcneTMJGv7ttsnOSnJRf33nfr2JHlbkouTnJVk36mfc1j/+IuSHLbYuiRJkoZgVj1jj66qfapqTX//CODkqtoTOLm/D/B4YM/+63DgKOjCG3Ak8BDgwcCRkwAnSZI0Zkt1mfIg4Lj+9nHAk6fa31OdrwC3S3JH4ADgpKq6uqq+D5wEHLhEtUmSJK0YswhjBZyY5PQkh/dtu1TVd/rb3wV26W/vCnx76rnr+7aNtW8gyeFJ1iZZe+WVV86gdEmSpLa2m8HP+JWquizJLwEnJblg+mBVVZKawe+hqo4GjgZYs2bNTH6mJElSS4vuGauqy/rvVwAfpRvzdXl/+ZH++xX9wy8D7jz19NV928baJUmSRm1RYSzJrZLcZnIbeBxwDnACMJkReRjw8f72CcAz+1mV+wHX9JczPwM8LslO/cD9x/VtkiRJo7bYy5S7AB9NMvlZx1fVPyc5DfhgkucAlwJP7x//KeAJwMXAj4BnA1TV1UleA5zWP+7VVXX1ImuTJEla8RYVxqrqEuD+87RfBew/T3sBL9rIzzoWOHYx9UiSJA2NK/BLkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKmhLQ5jSe6c5HNJzktybpKX9O1/muSyJGf2X0+Yes7Lk1yc5MIkB0y1H9i3XZzkiMWdkiRJ0nBst4jnXg/8UVV9LcltgNOTnNQf+6uqevP0g5PcGzgYuA9wJ+CzSe7RH/4b4LHAeuC0JCdU1XmLqE2SJGkQtjiMVdV3gO/0t/8zyfnArgs85SDg/VX1E+CbSS4GHtwfu7iqLgFI8v7+sYYxSZI0ejMZM5Zkd+ABwFf7phcnOSvJsUl26tt2Bb499bT1fdvG2uf7PYcnWZtk7ZVXXjmL0iVJkppadBhLcmvgI8DvV9W1wFHA3YB96HrO3rLY3zFRVUdX1ZqqWrNq1apZ/VhJkqRmFjNmjCTb0wWx91bVPwJU1eVTx48BPtnfvQy489TTV/dtLNAuSZI0aouZTRngncD5VfWXU+13nHrYU4Bz+tsnAAcnuXmSPYA9gVOB04A9k+yR5GZ0g/xP2NK6JEmShmQxPWMPB34LODvJmX3bnwCHJNkHKGAd8HyAqjo3yQfpBuZfD7yoqm4ASPJi4DPAtsCxVXXuIuqSJEkajMXMpvwSkHkOfWqB57wOeN087Z9a6HmSJElj5Qr8kiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLU0HatC5AkSUtn9yP+aVl/37o3/tqy/r4xsGdMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ25UbgkzcDYN2NezvMb87mBG2nrpra6MDb2PzpfMGfH85utsZ+fJG0pL1NKkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGloxYSzJgUkuTHJxkiNa1yNJkrQcVkQYS7It8DfA44F7A4ckuXfbqiRJkpbeighjwIOBi6vqkqr6KfB+4KDGNUmSJC25VFXrGkjyNODAqnpuf/+3gIdU1YvnPO5w4PD+7j2BC5exzJ2B7y3j71tuYz6/MZ8beH5D5/kN15jPDTy/WbtLVa2a78CgNgqvqqOBo1v87iRrq2pNi9+9HMZ8fmM+N/D8hs7zG64xnxt4fstppVymvAy489T91X2bJEnSqK2UMHYasGeSPZLcDDgYOKFxTZIkSUtuRVymrKrrk7wY+AywLXBsVZ3buKy5mlweXUZjPr8xnxt4fkPn+Q3XmM8NPL9lsyIG8EuSJG2tVsplSkmSpK2SYUySJKkhw5gkSVJDhjFJaijJNkl2bF2HtDXot19ccRzAv4AkAQ4F7lpVr06yG/DLVXVq49JmYqznl+QPFzpeVX+5XLXoF5fknnQ7bezVN50PHFNVy7njxpJKcjzwAuAGuqV9dgTeWlV/0bSwRUpy1sYOAVVVey9nPcspya2r6r9a16GFJbkE+Ajwrqo6r3U9E/aMLexvgYcCh/T3/5NuQ/OxGOv53WYTX6OU5OzWNSxWkocCn6f7t3g0cAzwQ+BzSfZrWNqs3buqrgWeDHwa2AP4rbYlzcTP6ALmPwBPB/5n//XE/vuYrZg39i2R5Ook70iyf/9BfazuD/w78I4kX0ly+EromV4R64ytYA+pqn2TnAFQVd/vF6Udi1GeX1X9WesalkqSp27sEPDLy1nLEvk/wCFV9fmpto8l+RfgSODxTaqakSTbVdX1wPZJtqcLY/+3qq5LMvjLFFW1T5K96D7gHU8XUI4HTuzPe9AW6HUPcOvlrGUJXAmcCbwaeE+SDwPvq6qvtC1rtqrqP+k+5B2T5H/Q/fv8q/58X1NVF7eoy56xhV3XX18ugCSr6D75jcWozy/JPZKcnOSc/v7eSV7Zuq5F+gDwJG7scZjuedihYV2zcrc5QQyAqvoCcNflL2fmJkMA/h5YB9wKOCXJXYBrWxU1S1V1QVUdWVX7Ap8A3gP8QeOyZuX1wE7ctLf91gz//fSHVfV/q+rhdFdMLgP+NsklSV7fuLaZSbJtkicl+Sjw18Bb6F5bPgF8qlldjhnbuCSHAs8A9gWOA54GvLKqPtS0sBnZyPm9qqo+2LSwGUnyBeClwN9X1QP6tnOq6r5tK9tySU4HDquqc+Y59u2quvM8TxuMJKdX1QM3cuxr/Rv8YCU5Y/JvcZ5j242k92hXui3tngJ8H/gg8NExjKdK8mXgd6vq9HmODfrvb2P/NvuezmeM5YpDP2bsc8A7q+rLc469rap+r0ldhrGF9f8Q96frhj65qs5vXNJMjfn8kpxWVQ+afpFJcmZV7dO6ti2V5BHApVX1rXmOramqtQ3KmpkkVwDvn+8Q8PSq2mWZS5qpJOuBjU4gGfrkkv4D0G3oAthHgKumj1fV1S3qmpV+cslVVfW9eY7tUlWXNyhrJpL8ZVUtOPlpDFbqRAvHjM0jye2n7l4BvG/62NBfUCaS/ENV/RZwwTxtY/C9JHfjxsuwTwO+07akxamqLy5wbNBBrPfSBY6N4fy2pbukNdYB0neh+3t7Pt2M2In07YO+1LzQjN4hBzGArSGI9e6U5Chgl6q6b5K9gSdV1WtbFmXP2DySfJPuhWO+F8yqqkG/oEzMvezTjx87u6ru3bCsmUlyV7oZeQ+ju1zyTeA3q2pdy7pmoR/f9zxgd6Y+VFXVb7eqSZs2hkutC0lyl6q6tHUdSy3JPeg+ONyFDf/+HtOsqBkZ+2vLSh2+Ys/YPKpqj9Y1LKUkLwf+BLhFkmu5MXT+lBW0i/1iVdUlwK8muRWwTT+LZiw+DnwR+CzdUgKjl+Toqjp8049c0ebtEUtyZ+Dgoa8zBnyUbgzq2H0I+Du6WXlj+/sb+2vLLavq1DmrdzQfq2kY24R+KYFfoesp+2JVfaxxSYtWVW8A3pDkDVX18tb1zNrGpp9P/viGPi6nd8uq+uPWRczanCECGxwCnrCctSyR/Sc3+h6IX6dbBuJOdEFm6MZ6+XWu66vqqNZFLJFRvrZMWZHDVwxjC0jyt8DduXHM2AuSPLaqXtSwrJmpqpcn2QnYk6llEarqlHZVzcRkYdd7Ag8CTujv/09uXFpg6D6Z5AlV1Wwq9hK5EriUDd/UJ0MGfqlJRbN1XZLDgN8A7gH8I7BHVa1uW9bM7JrkbRs72Gqm2qxMfVj4RJIX0gXon0yOj2Q88VhfWyZeRHcFaK8kl9ENXzm0bUmOGVtQkguAe1X/HynJNsC5VXWvtpXNRpLnAi8BVtMt9rcf8G9jGPcAkOQU4NcmlyeT3Ab4p6p6ZNvKtlyS/+TGcHIrujeC67hxu5nmK0kvRpKLgP03Mlt00EsHACT5b7oPBK8EvlRVleSSEY1DvZRu4d55VdVxy1jOzI15PPHYX1smkjywqk6fHr6S5IlV9cmWddkztrCLgd3oPqkD3LlvG4uX0PUcfaWqHt0vczGaxf2AXejGwU38tG8brKrarO2cktynqs5d6nqWwF/TLap5kzAG/Pky17IUXk63BtffAu9L8oHG9czaVUMPXAvZ3PHE/RWUk5a6nlnaCl5bJo5J8szJWo1JDqZblLhpGBv6isFLIsknkpxAd7nr/CSfT/I5ug2Lx7S34Y+r6scASW5eVRfQXdobi/cApyb50yR/BnwVeHfbkpbNP7QuYAsdXVVfn+9AVb19uYuZtar666raDziob/oY3VT7P+5n6A3dTzf9kK3Cm1oXsISG+toy8TS67Z72SvI8usuWj2tck5cp59PvV7VR/dYsg9dvB/Fs4PeBx9At/7B9VY1hoDQASfYFHsGNEzDOaFzSslhopfeVrF/09QS6cZr/UlvBC1SS+9IN4n9GVd29dT2LkWR34PtVdU1//9F0+29eSrcH51YR1ob697c5xnBu/Qefj9H1wHyWKZ8AACAASURBVD+lqv67cUmGsc2Rbkf36fVWxjBIcwN9AL0t8M9jesFMcn/gkdwYxubtdRmboa5nleQOdJ9cD6abWPIRRrRZcZK9+h7oSW/0T6aOPbSq/q1ddYuX5Kt0b27/kWQfuuUR3gDsDVxXVc9tWuAyGerf3+YY6rklOZt+BmXvl4Br6CdgVNXeLeqaMIwtIMnhdDvY/5huA+3JQMbBDtKELlxW1bUbW0ZgLGEzyUvoFi/8CN3/u6fQXQYb/OWuTRnqC+a0JHeiW/rhYLoXzvdX1SvaVrU40/9f5ll0eQz/z86avKkleTPws6p6WT/56czWb3jLZQz/LzdmqOeW5C4LHW+9WLED+Bf2UuC+Nc8+ZAN3PPBE4HRunD0z/X3QYXPKc4CHVNUPAZK8Cfg3YPRhjBGM3el7V95Jd/n8D4HnAoMOY2w4C2/ujLwxrNE1fQ6PoZuwQFX9bM4im2O3rnUBS2iQry1VdWm/y8y5VbVX63rmMowt7BvAj1oXMWtV9cR0r4z/Y74lBEYkbLiC9A0M/A0vyd5VddamHtcPEh+kJDvQrQl3CN1WVv8MHAEManbaRtRGbs93f4j+JckH6RbR3An4F4Akd2Sgb+Jz9bPODwJ27ZsuA06oqvMnj6mqp7aobbGS3BY4kA3P7TNV9YPJY4b82lJVNyS5MMluK+29zzC2sJcDX+7HQUwv7DfohQuhu9aa5J+A+7WuZQm9C/hqP1EBuoHE72xYzyyckeQS4P10Y6nOa13QLCU5HvhV4AvAe4HfmMz4HYnV/aKombpNf3/XjT9tMH4feAZwR+BXquq6vv2X6bZgG7Qkf0z3IeH93LiA9Gq6ZUreX1VvbFbcIiV5JnAkcCJdCAN4NPD6JH9WVe9pVtxs7QScm+RU4IeTxqp6UruSHDO2oP5/1peAs+nGjAHDX7hwIslxdDOcTmtdy1LpZ1P+Sn938LMpk5wB/Bb97Du6F5P30Y2nWtewtJno3xA+WuPaR/Tn+tX3N2osry3zSfKvVfXw1nUsRpJ/B+4zFTIn7Teju/y1Z5vKFi/JhXTDOn4wp30n4KtVNYalVza6WkLrVRLsGVvY9lU17z6HI/EQ4NB+1ewfcuMEhTENsv0m3Saw2wFJsm9Vfa1xTYtR/WKFrwBekeTBdAPcv5TkW1X1sLblzcRTNja+aOifzscctjbDbq0LmIGf0e0jOnew9x2Z+sA+UJMxw3NNJq+NQuvQtTGGsYV9up9R+QnGt/8YwAGtC1hKSV4DPItu7N/kRaboBhYP1QYvilV1Kt3Ctn9Et4TH0D1oI+1PoruMN+gwlmRnukUmvw8cC/wF3Tp43wD+qKrGtMPHXGO4DPP7wMn9tl3f7tt2o9vD+MXNqpqN1wFfS3IiG57bY4HXNKtqxqa2fQK4GbA98MPW2z15mXIB/T5kcw1+aYu5kvwSG24UvqIGNm6pvtv9fiNbN+03qur41nUsh36SyaHAHwPnAa/bnMkLK1n/RreWbieP/enGNX6CLpAdWlWPalfd4iXZ2MD1AH9XVauWs56l0C/T8WA2HOR+WlXdsPFnDUN/SfIAbjqA//vtqlo6/WvMQcB+VXVE01oMY1uvJE8C3kLX7X4FcBfg/Kq6T9PCZiTJR4DfqaorWtcya0n2B768ElaOnrUk29H1aP5v4CvAG6rqwqZFzUiSr1fV/fs3gUurarepY2dW1T4Ny1u0JO9a6HhVPXu5alkqfRibLNdxM+C+wLoRXTH5uSS3H+N5zbUSdhXwMuUCktySbn2j3arq8CR7Avesxru7z9BrgP2Az1bVA/qtS36zcU2z9Aa62YfnsOFl5qazZmbkmcBRSa4GvgicAnxp6J9gk7yIbgP7k4EDxzApYY4b4OezmeeuXzj0MUebHbaSHDbE8XNJngz8PfCzJC+gmyH6X8A9k/xOVX2iaYGLkOThwDvo/h3+NvBa4K594Hz60HeHmJjTe7sNsIZuYfem7BlbQJIP0C2M+syqum8fzr489E+vE0nWVtWaJF8HHtB/0vt6Vd2/dW2zkORcuhfOubNhV+QAzi3Rr1L/NLpepDtV1aA/YCX5GV0v7ZVsOMZoFJNLkvyALjiH7tLkKZNDdEtB7NSqtuU04FXczwAeD9wC+DrwoKq6sF/d/SNVtaZpgYvQrx7wHODWdJfOn1xVX+pnpL996DNhJ+b03l5Pt0DvMa2voAz6hXsZ3K2qnpHkEICq+lHGtYz0D5Lcmu4N4b3pNmn+4SaeMyQ/qqq3bfphw5PkN+nezO8HfA/4v3Q9ZEO3R+sClthBU7ffPOfY3PtjNtjX0ar6LkA/e/nCvu3SyeXLAdu+qs4GSHJlVX0JoKq+luQWbUubqXdU1b9ON/S9goaxFeyn/T/CAkhyN6Yud43AQcB/A39AN1D6tnR7cY7FF5O8ATiBDS9TDnlpi4m/ppuB93fA58ZyOW+yP1ySPYDJ2MXzquqSdlXNzph6ZRdpsJdkkmxTVZNLeZO2belm5g3ZdJh8+ZxjQz+3aW8H5vbKzte2rAxjCzuSbiuWOyd5L/BwuoHFY/F84ANVdRkwuPEbm2EyIHN6+46hL20BQFXtnOQ+dMtZvK4fz3hhVf1W49IWJcmOdONW1gBn9s37JDkdeE5VXdusuBlI8jk2HkSqqvZfznoaGmrP2OF0weTH/bIyE3cGBrv6fu9VSW5ZVT+qqo9NGvtOiEEvKQOQ5KF026utSjK9fuiOwLZtqrqRYWwBVXVSkq/RvZkHeEmNa9Pw2wAn9oPAPwB8qKoub1zTzFTVoxc6PtRBxPDz0LIb3QzY3el6NQc/ABx4G90yFgf3vQ+T6eevorsU+8yGtc3C/56nbT/gZTS+TLLM/nXTD1l5JruVzJ3N3PdMr2tX2eJV1QnQha+q+sZU+zeAP29W2OzcjG483HZ0730T19KNu23KAfwL6K8jn1lVP+zH6OwLvHVyKWUskuxNt7XO/wLWV9WvNi5pWQx1EDFAkrPotur6EnBKVa1vXNJMJLloY1vKLHRsiPptWV5Ft8bf66rq041Lmrkkv0K3Jtc5VXVi63pmpd9K7qHAqGYzAyT5At1+m6fRn9tkLNkYJLnLQu/hSd5eVb+7nDXBhteIdVNHAT9Kcn+6JS6+wQi6a+dxBfBd4CrglxrXspyGeqmEqtq7ql5YVcfPF8SSvL1FXUtssP+/piU5IMkX6YLY66rqV8YSxPoZeZPbz6PrzbwNcGSSpotqzlJVHVbdXo1PpVut/m/oZgAPXlX9D+BedOOobgf8U3/1ZBQ2ozOlyaxRL1Mu7Pp+PaCDgL+pqncmeU7romYlyQuBpwOrgA8Bz6uq89pWtazG3C081GnoX07yf4DX1FS3fZJXAYNf5yjJaXR/b39Bfz790gHAKCaXbD91+3DgsVV1ZZI30y3gO/RxVcCoZzNPejMf0X/dDvgkIzm3lcwwtrD/TPJyuoVQH9lPXd5+E88ZkjsDv19VZ27ykeM0ip6Wkfld4J3AxUl+PoAfOAN4brOqZueHdIuEPo1uWMD0v8ExTC7Zpt9SZxu6YTBXAvRDPa5vW9pMjXI2c+/zdOtrvgH4VI1oO7mVzDC2sGcAv0E3i+u7SXaj+0Q7aEl27Gel/UV///bTx7eG7S96gxxEPGb9v8tf72dw3btvPm96QPGQ1WbuPZnksVV10hKXsxRuS/dGHqCS3LGqvtOvZziaDz9jnc3c25muZ/2RwO/1CzH/W1W9qm1Zy6bJv1PD2AL6xf3+cur+txjHmLHjgSfSvWgWN/10PoqN0JPsAryebmX6xye5N/DQqnonQFW9uGmBS2uQb3xJDgBuU1Ufput5mLQ/DbhmoAFlS7wJGNy5VtXuGzn0M+ApkztJdhryYPcRz2amqn6Q5BK6Kyer6ZaDGNMVoU15a4tf6mzKBfR7WL2JblB7uHFLlh2bFqbNkuTTwLuAV1S3OfN2wBlVdb/Gpc3MZF2gedqfVVXvblDSoiT5V7ptWK6c074z8ImqemibypbXSti4eCkNeSYzjHc2M0AfxC6gGyf2ReDUMVyqTHJbusVsn0z3nl50k9c+Dryxqn7QsDxnU27CnwNPqqrbVtWOVXWbMQWxJCdvTtuA7VxVH6T/xFpV19Nv1Dx0SR6W5Dy6F02S3D/J306ODzGI9W4+N4gB9Ov73apBPa2M/VPyIHtuJ0Y+m/nuVfWEqnpDVX1pbhDrx1EP0QeB7wOPqqrbV9UdgEf3bR9sWhmGsU25vKrOb13ErCXZoR8ntnOSnZLcvv/aHdi1bXUz9cMkd+DG7az2A65pW9LM/BVwAN1yJFTV1+nGeAzdjn0P5gaSbE+3ObPGYexhc6izmZkstryAX1+WQmZv96p6Uz/8COiGIlXVm+guNzflmLGFrU3yAeBjbLi34T+2K2kmng/8PnAnYHoq/bV0U7TH4o/o9qW8W3/5axUrYKXlWamqb8/Zt34MvX7/CByT5MVV9UOAfvD3W/tjW4t1rQuQNmKovZqXJnkZcNxkp5l+XPGz6NaKa8owtrAdgR8Bj5tqKwb+plBVbwXemuR3q2rI3ekLqqrT+1XO70n3AnJhVV3XuKxZ+XaSh9HNWNseeAkwhl7cVwKvpXvhnCzOuBvdcheDn82V5GbAwcB/VNVnk/wG3QDp84GjJ/8+q+qpDctcDkN9Q9dwezWfARwBfCHJZHHzy+k+sD+9WVU9B/BvhfqJCRs1gp4/4OeDbN9Ptxn6KJZGmOgHtL8V+FW6N7YT6fZOvappYTOS5BbA3fu7F0/2AJw6PsilH5K8l+5D8C2BH9DtlfePwP50r8eHNSxv0eYukzPXZNmcJLcf8xI6Y56AMeZza8kwtoAk96DbEmmXqrpvv4fjk6rqtY1LW5Qk71rgcFXVby9bMUsoyV3oPg09g24Q/weAD/ZLlGjAhjobL8lZVbV3Py7uMrplV27oN0P/elXt3bjERUnyTW66XM5EVdUols2ZGNts5s2R5E+q6vWt65ilJM+uqoXeF5e+BsPYxvUbpr4U+PvJJ4Ek51TVfdtWpl9Uvyjjq4BDq2rb1vUsVpI/p7uc99/APwN7A39QVf+vaWHLZKifzpOcA+xLNzP0W8BdqurqJDvQLbtyr6YFarP0QwTeAdy6qnbr9y9+flW9sHFpizbWToiFJPlWVe3WsgbHjC3sllV16pxB0qPZ0qPfA/AmqurVy13LUpnTO3YD8LK2Fc3M46rqZUmeQjfY+6nAKcBWEcYY7riVd9ItR7It8ArgQ/26TvvRXVIfhb6n71Bgj6p6Tb97yS9X1ambeOpQTGYznwDdbOYkY5jNDHAMfScEQFWdleR4ug9/g9UPW5n3ELDLctYyH8PYwr7Xb8syWRrhacB32pY0Uz+cur0D3ar8YxgEDkCSr9KtHP0h4Ner6pLGJc3S5G/314APVdU1cz40aAWqqr/qZ2hTVf+R5D104/6OmQ4qQ1+hHvhbuqEBjwFeA/wn8BHgQS2LmqWRzmaG8XZC7EIXoOf+XQX48vKXsyHD2MJeBBwN7JXkMuCbdJ/2RqGq3jJ9P8mbgc80KmcpPLOqLmxdxBL5ZJIL6C5T/k6SVcCPG9e0nNa1LmBLVdV/TN3+AfDheR52Mt3lzKF6SFXtm+QMgKr6fj+TdCzGOpsZxtsJ8Um6y8pnzj2Q5PPLX86cGhwztmlJbgVsU1X/Oaf9sKo6rlFZM5dkJ+C0qrr7Jh+8giX5zar6f0n+cL7jVfWX87UPTT9z7Zp+APgtgR2nFzQciyTvqapntq5jOQ11TNxE3yv9MLrXk337DwsnDvmcpo15NnOSu9J1QjyMrhfpm8BvVtW6lnUtl1a90vaMbYbJ4pPzeAkw2DCW5GxuHHuzLd2iqGMYLzbZNuc2TatYensBu89ZsX7QG9knOWFuE/DoJLcDqKonLX9VTQz9U/LbgI8Cv5TkdXSLLb+ybUmz02/PNZqrJNP64Ry/urFOiK1Ak15pw9jiDH2QzhOnbl9Pt/3T4McGVNVk4Omfta5lqST5B+BuwJncOFalGHgYA1YD59HNVJsskbAGeMtCT9LKUlXvTXI6/fppdJu/j+Uy3qhnMyd5CfAuunF+xyTZFziiqk5sW9myafK+7mXKRRjqWkfT+kuTd2YqmFfV1zb+jOHolwt4DnAfugkKAIxhHbUk5wP3rpH9ASfZhq7H+QnAS6vqzCSXjG19qk0Z6mXKzV30deiSnFlV+/SzmZ8I/CFwSlXdv3Fpi5bk61V1/yQHAC+g69H8h6G/122uVu/r9owtzqB7xpK8hm5frm9w42WRopsBNQb/QLeMwAF0l18PZTyDbM8BfplxDKz9uX6T4r9K8qH+++WM6HXqFwgr+y9DOUvhdG7s0dyNbsxRgNvRrau2R7vSZmrMs5knJ/IE4D1VdW5GdHIr1Whe5Br519YFLNLTgbtV1U9bF7JE7l5Vv57koKo6rl8r54uti5qRnYHzkpzKhpvYj2JMVVWtB349ya/RbWC/gQEv/TAdVuYq4K4w3B6kqtoDIMkxwEer6lP9/ccDT25Z24yNeTbz6UlOpAvOL09yG7plSrYWXqZcSfoZJU+lu4R3A/DvwPFVdZM3hqFK8hHgd6rqita1LIUkp1bVg5OcArwQ+C5w6hgueaXbAP0mquoLy11LC2MYIjBmSc6uqvttqm3IxjqbuR8qsA9wSVX9IMkdgF2ramOLpg7C5vZKp9G+qfaMzSPJ79GNAziFbpHCM+hC2VeSvLCqPt+wvFl6A3BGv0XL6HpXgKP7MXGvpFsp+9Z0WyINXlV9od9dYM+q+mz/ZjD4bZ5+AYO+bLIVrFD/H0leyY07QhwK/McCjx+i0c1mhm6oQD884N5zzm3oVnSvtD1j8+iXfNhn6hPPp6rqUf0L5seHOLB2PknOpdvy4mymuqHH0ruS5ObA/wJ2p1uJH7rNige/fEeS5wGHA7evqrul23vz76pqqGONfiFD7xlLchT9CvVVda/+Q8OJVTWKFer7XogjgckWQacAfzbUy69zbWw2c1X9XruqZiPJm+i2jzuPDc9tLB/SV6Qxpd5Z247uH+LN6XpUqKpv9astj8WPquptrYtYQh8HrqH7RPSTTTx2aF4EPBj4KkBVXZTkl9qWpF/AqFeo70PXS/rxRlVV/9W6phlbwwhnM/eeDNyzqsb2mgms3F5pw9j83gGc1q8i/QjgTQD9IM1RfLLrfTHJG+gu4U1fphzF0hbA6qo6sHURS+QnVfXTySSn/nLCGN8YNmbQlymB65Jsy41bzqxiRIOkk9yP7pLd7fv73wMOq6pzmhY2O6Oczdy7hO5KwijDGCt031TD2Dyq6q1JPgvcC3hLVV3Qt1/Jjd3uQ57RNTG53LrfVNuYlrb4cpL7VdXZrQtZAl9I8ifALZI8lm6Cwica17RoW8HSDxOjXqGebvjDH1bV5wCSPIobt9gZgzHPZv4RcGaSk9nw3AZ/Cba3InulHTO2CEMft7IpGfjem0nOA+5Ot7faT+h6U6qq9m5a2Az0M56eAzyO7rw+A7xj6JdNknyTBQbZjmEm7ESSvbhxhfqTR7ZC/dfnLoA6X9tQjXk2c5LD5msf8nvBtKzQfVMNY4sw1FWyN9fQw2Y/2/AmqurS5a5Fgq1qhfqPAl+jW3gZ4DeBB1bVU9pVNVvzzWaukezjmOQWwG5VdWHrWmYtyaF0ExT2pdtb+mnAK6vqQy3r8jLl4ow9yQ56XM4YQ1c23Nz9JsbQ6wcrd5DtDGwtK9T/NvBndGNxoFts+dntypmt6dnMdLMqdwX+juFfPifJ/wTeDNwM2CPJPsCrR3IJdsXum2oY00LGHjaHaLK5+4v679M9D2P6/7UiB9ku1la0Qv3d6NZm3IbufWZ/uv+Xo/iwwLhnM/8p3bl9HqDfH3bwwwPm9EpfAbxv+ljrXmnD2OIMuudoM4z9/AZn0tuX5LFzLpH/cZKvAUe0qWzmVuQg2xnar6qeN7lTVZ9O8uctC5qx9wL/m27W4WhmiU4Z82zm6+bZa3MM/w9XdK+0YWweW9GMrk0Z+t6bY5YkD6+qf+3vPIyuF2IsRr30A+Nfof7Kqhr87N4FjHI2c+/cJL8BbNsvJv17wJcb17RoK71X2gH88xj7jK4kDwHOr6pr+4GaR9ANZjwPeH1VXdO0QG1SkgcCxwK37Zt+APz2WNaIW6mDbGdlK1ihfn/gEGDu8gj/2KyoGRrrbGaAfjLCK9jw3F5TVaPYCD0rdN9Uw9hWqN8G6f5VdX2So+nWlfkwXU/f/avqqU0L1GZLcluAuQF66MuSwLiXfpgY6wr1Sf4f3d6N53Jjj2ZV1W+3q0qCJJ+hm1Ay3Sv9yKo6oF1VhrEFjXVGV5Lzq+pe/e0Nlq9IcmZV7dOuOs3CUJcl2YqWfthghXpgVCvUJ7mwqu7Zuo5ZG/Ns5iSfYOFzG8VsypXaK+2YsYWNckYXcE6SZ1fVu4CvJ1lTVWuT3AO4rnVxmomhTr5Y0YNsZ2jsK9R/Ocm9q+q81oXM2JhnM7+5//5Uuq2eJj1HhwCXN6loCazUfVPtGVvApHdhenHXMawi3V/aeivdvpvfoxuX8+3+6/eq6usNy9MMDLVnbGJjg2yr6vltK5uNrWCF+vPplrcY3e4XMP+C30P/m5tIsraq1myqbahWaq+0PWMLG+WMrn580bOS7EjX07AdsL6qNvj0M4K9N7dmQ+0Zmxj70g+XJHkVG/asXNKwnlk7sHUBS2zMs5lvleSuVXUJQJI9gFs1rmmWVmSvtGFsYaPezLeqrgUW6gU7ma7XTMMz9GVJxr70w6hXqB/j7hdzPAc4djKBhn42c8N6ZukPgM8nuYTuQ91d6HYbGItbTYIYQFV9PknzsOllyk3YGmZ0bczY994cm6lxgIO3UgfZzkqSNXTLB+zOjR+KR3MZb2sx1tnMSW5ONxsW4IKq+snUscdW1UltKlu8lbpvqmFsHlvLjK5NGcsYiK1Fkm9V1W6t65illTbIdlaSXMg8K9RvBT1KW4Uxv3YO/dyS7ETXK/3wvumLwJ9W1Q/aVeVlyo3ZWmZ0aWCSnLWxQ8Auy1nLUpo7yDbJihhkO0NjX6F+azf0MZsLGfq5rch9Uw1j81jp2yYso6H/0Y3RLsABdB8QpoURbFkyZUUOsp2hI5O8g5GuUK/BL3OxkKGf24rcN9UwtrBRzuhy781B+yRw66o6c+6BJJ9f/nKWzIocZDtDz6Ybk7M9UyvUA4axcfCD7Mq1InulDWMLG+uMrunLsHMVcFfYesbGDUlVPWeBY78xuT2CZUnGvvTDg8a4Qr1+buizmReyrnUBi7Qie6UdwL+Asc/o0ng5yHZlS/Iu4C9GuEL96PUz7HcFvjo9sSTJgVX1z+0qW7wkD6abMHNaknvTrRd3wWSozhis1H1TDWObYcQzuka596aGvyzJ2Jd+GPsK9WOV5PfotkI6H9gHeElVfbw/NvQPQEcCj6f7ezsJeAjwOeCxwGeq6nUNy5uZlbpvqpcpF7AVzOga696bcpDtSjf2FerH6nl0a1L9V5LdgQ8n2b2q3srwx4k9jS5g3hz4LrC6qq5N8mbgq8AowhgrdN9Uw9jCxj6j6yGTvTcBqur7SW7WuiiJFTrIdlZcT2ywtplcIamqdf17woeT3IXhh7Hrq+oG4EdJvtHv0EJV/XeSMX0g2g84M8mK6pU2jC1s7DO6Rrn3poDhvzGsyEG22updnmSfyWzmvofsicCxwP3alrZoP01yy6r6EfDASWO/y8CY3hdWZK+0Y8YWsFK3TZiVJIcCz6Dbf/I4+r03q+pDTQvTRm3usiRJbj/kiSYrdZCttm5JVtP1IH13nmPTG4cPbjZzkptPb3s01b4zcMeqOru/P7hzGwLD2ALGPqMLtu69N4eo71rf6LIkVXXXZS5pSazUQbbS5hj6YP6FjPncWvIy5cJW5LYJizWnd+UK4H3Tx4bcozJ2k90htgIrcpCttJmGPkxgIWM+t2YMYwsb64wu994cuK1gWZIVOchW2kxjvuQ05nNrxjC2sFHO6HLvzVEY+7IkK3KQrSQtBcPYwsY+o2uUe29uJUa9LIlLP2jgxnwpb8zn1oxhbGFj38x3rHtvbg1clkRaZps7m5lufPGgjPnchsDZlAsY+4wu994cLpclkZbfmGczj/nchsAwtoCtZTPfse69OXYuSyJJ42AYW8DYN/Odu/cmMLa9N0fnF7iUIGmJjHk285jPbSUzjC2g32/sJsYyuDjJl4FXzNl78/VVNZa9N0dnzqWEmyxLshWtQyY1k+Qo+tnMVXWvfoHwE6tq8LOZx3xuK5kD+BcwltC1gLHvvTk6LksirQhjns085nNbsbZpXYCauiTJq5Ls3n+9ErikdVHaLP+/vTtWjSoIozh+TqFNKkHTiYKdIDYKgnY2voJoYx7BdhW0MA9gp61pJYUvIBrSWYhYpLFMY+MbjMXd4LIE45Xdnbln/j/YYm81X7Hsx9z5ztw5acSkIZZEEjuawGYkTzMn19YsmrG+7Ui6pCEs9L2kixriPNC+Y9vPFhrpmYglATbltaR9Sdu2X0k6kLRbd0krk1xbszgz1jHbtyTNJF3Vn1fWMQMKyYglAepKnmZOrq1VNGMds32kU+7e7OCsXAxiSYDNSZ5mTq5tCjjA37fIuzd7sBxLYptYEmD9vugv08ySpjzNnFxb82jG+pZ+92ayN5KeLsWSvBWH+IG1SZ5mTq5tCnhN2THbexru3vyuhbs3Syk79VaFf2H7aynl5lnPAKye7W+llBtnPZui5Npaxs5Y324n370Z7oft55Lezb8/FrEkwKYcz6OA9ubfHylnmjm5tmYRbdG3Q9vXay8C/4VYEqCehxp+f/vzz/b8WYLk2prFa8qOpd+9mYxYEqC+5Gnm5NpaRDPWsfS7N5MRSwLUszzNLClmmjm5tpbRjAETZPuglHKv9jqAHtk+lDRbmmbeLaVMfpo5ubaWcYAfmCZigvAK4QAAAQtJREFUSYB6tk6aFUkqpXy0vVVzQSuUXFuzaMaAaXqiIZbknBZiSSTRjAHrlzzNnFxbs3hNCUyQ7SNiSYA6bF+Q9FLS3fmjz5JelFJ+1VvVaiTX1jKiLYBpIpYEqOeapMsa/kPPa7hU+1PVFa1Ocm3NYmcMmCBiSYB6kqeZk2trGWfGgGl6UHsBQMd+llI+1F7EmiTX1ix2xgAAGMH2fQ2p9HHTzMm1tYydMQAAxkmeZk6urVnsjAEAMELyNHNybS1jmhIAgHGSp5mTa2sWO2MAAIyQPM2cXFvLaMYAABjB9pXTnifEPyTX1jKaMQAAgIo4MwYAAFARzRgAAEBFNGMAAAAV0YwBAABU9BttyghgMzTWtAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ke0mH9fr5HP1"
      },
      "source": [
        "The majority of our deep learning models perform on par or only slightly better than the naive model. And for the turkey model, changing a single data point destroys its performance.\n",
        "\n",
        "> 🔑 **Note:** Just because one type of model performs better here doesn't mean it'll perform the best elsewhere (and vice versa, just because one model performs poorly here, doesn't mean it'll perform poorly elsewhere).\n",
        "\n",
        "As I said at the start, this is not financial advice.\n",
        "\n",
        "After what we've gone through, you'll now have some of the skills required to callout BS for any future tutorial or blog post or investment sales guide claiming to have model which is able to predict the futrue.\n",
        "\n",
        "[Mark Saroufim's Tweet](https://twitter.com/marksaroufim/status/1366871736604532739?s=20) sums this up nicely (stock market forecasting with a machine learning model is just as reliable as palm reading).\n",
        "\n",
        "![Mark Saroufim tweet on what forecasting with a machine learning model reminds him of: palm reading, basic heuristics, comparing calculations in the brain of different organisms](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/10-mark-saroufim-tweet-forecasting-bs.png)\n",
        "*Beware the tutorials or trading courses which claim to use some kind of algorithm to beat the market (an open system), they're likely a scam or the creator is very lucky and hasn't yet come across a turkey problem.*\n",
        "\n",
        "Don't let these results get you down though, forecasting in a closed system (such as predicting the demand of electricity) often yields quite usable results.\n",
        "\n",
        "If anything, this module teaches anti-knowledge. Knowing that forecasting methods usually *don't* perform well in open systems.\n",
        "\n",
        "Plus, sometimes not knowing the future is a benefit. A known future is already the past.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47VA8gR3B7QP"
      },
      "source": [
        "## 🛠 Exercises\n",
        "\n",
        "1. Does scaling the data help for univariate/multivariate data? (e.g. getting all of the values between 0 & 1) \n",
        "  * Try doing this for a univariate model (e.g. `model_1`) and a multivariate model (e.g. `model_6`) and see if it effects model training or evaluation results.\n",
        "2. Get the most up to date data on Bitcoin, train a model & see how it goes (our data goes up to May 18 2021).\n",
        "  * You can download the Bitcoin historical data for free from [coindesk.com/price/bitcoin](https://www.coindesk.com/price/bitcoin) and clicking \"Export Data\" -> \"CSV\".\n",
        "3. For most of our models we used `WINDOW_SIZE=7`, but is there a better window size?\n",
        "  * Setup a series of experiments to find whether or not there's a better window size.\n",
        "  * For example, you might train 10 different models with `HORIZON=1` but with window sizes ranging from 2-12.\n",
        "4. Create a windowed dataset just like the ones we used for `model_1` using [`tf.keras.preprocessing.timeseries_dataset_from_array()`](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/timeseries_dataset_from_array) and retrain `model_1` using the recreated dataset.\n",
        "5. For our multivariate modelling experiment, we added the Bitcoin block reward size as an extra feature to make our time series multivariate. \n",
        "  * Are there any other features you think you could add? \n",
        "  * If so, try it out, how do these affect the model?\n",
        "6. Make prediction intervals for future forecasts. To do so, one way would be to train an ensemble model on all of the data, make future forecasts with it and calculate the prediction intervals of the ensemble just like we did for `model_8`.\n",
        "7. For future predictions, try to make a prediction, retrain a model on the predictions, make a prediction, retrain a model, make a prediction, retrain a model, make a prediction (retrain a model each time a new prediction is made). Plot the results, how do they look compared to the future predictions where a model wasn't retrained for every forecast (`model_9`)?\n",
        "8. Throughout this notebook, we've only tried algorithms we've handcrafted ourselves. But it's worth seeing how a purpose built forecasting algorithm goes. \n",
        "  * Try out one of the extra algorithms listed in the modelling experiments part such as:\n",
        "    * [Facebook's Kats library](https://github.com/facebookresearch/Kats) - there are many models in here, remember the machine learning practioner's motto: experiment, experiment, experiment.\n",
        "    * [LinkedIn's Greykite library](https://github.com/linkedin/greykite)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiOlVRREB7No"
      },
      "source": [
        "## 📖 Extra-curriculum\n",
        "\n",
        "We've only really scratched the surface with time series forecasting and time series modelling in general. But the good news is, you've got plenty of hands-on coding experience with it already.\n",
        "\n",
        "If you'd like to dig deeper in to the world of time series, I'd recommend the following:\n",
        "\n",
        "* [Forecasting: Principles and Practice](https://otexts.com/fpp3/) is an outstanding online textbook which discusses at length many of the most important concepts in time series forecasting. I'd especially recommend reading at least Chapter 1 in full.\n",
        "  * I'd definitely recommend at least checking out chapter 1 as well as the chapter on forecasting accuracy measures.\n",
        "* 🎥 [Introduction to machine learning and time series](https://youtu.be/wqQKFu41FIw) by Markus Loning goes through different time series problems and how to approach them. It focuses on using the `sktime` library (Scikit-Learn for time series), though the principles are applicable elsewhere.\n",
        "* [*Why you should care about the Nate Silver vs. Nassim Taleb Twitter war*](https://towardsdatascience.com/why-you-should-care-about-the-nate-silver-vs-nassim-taleb-twitter-war-a581dce1f5fc) by Isaac Faber is an outstanding discussion insight into the role of uncertainty in the example of election prediction.\n",
        "* [TensorFlow time series tutorial](https://www.tensorflow.org/tutorials/structured_data/time_series) - A tutorial on using TensorFlow to forecast weather time series data with TensorFlow.\n",
        "* 📕 [*The Black Swan*](https://en.wikipedia.org/wiki/The_Black_Swan:_The_Impact_of_the_Highly_Improbable) by Nassim Nicholas Taleb - Nassim Taleb was a pit trader (a trader who trades on their own behalf) for 25 years, this book compiles many of the lessons he learned from first-hand experience. It changed my whole perspective on our ability to predict. \n",
        "* [*3 facts about time series forecasting that surprise experienced machine learning practitioners*](https://towardsdatascience.com/3-facts-about-time-series-forecasting-that-surprise-experienced-machine-learning-practitioners-69c18ee89387) by Skander Hannachi, Ph.D - time series data is different to other kinds of data, if you've worked on other kinds of machine learning problems before, getting into time series might require some adjustments, Hannachi outlines 3 of the most common.\n",
        "* 🎥 World-class lectures by \n",
        "Jordan Kern, watching these will take you from 0 to 1 with time series problems: \n",
        "  * [Time Series Analysis](https://youtu.be/Prpu_U5tKkE) - how to analyse time series data.\n",
        "  * [Time Series Modelling](https://www.youtube.com/watch?v=s3XH7fTHMb4) - different techniques for modelling time series data (many of which aren't deep learning)."
      ]
    }
  ]
}