soyforecast.ipynb

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  "metadata": {
    "colab": {
      "name": "soyforecast.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [
        "e4STNFJvTIfB"
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      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/marlonrcfranco/soyforecast/blob/master/soyforecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9hjvEuBTIS_",
        "colab_type": "text"
      },
      "source": [
        "# Soyforecast\n",
        "By [Marlon Franco]()\n",
        "/ [GitHub](https://github.com/marlonrcfranco/soyforecast.git)\n",
        "\n",
        "####Prediction of future soybeans stock prices on the Chicago Stock Exchange (CBOT) using a LSTM model and relating climatic data from the most productive regions of the USA.\n",
        "\n",
        "![alt text](https://github.com/marlonrcfranco/soyforecast/blob/master/Selection_244.png?raw=true)\n",
        "\n",
        "\n",
        "## Soybean, CBOT Soybean Futures + ( Global Historical Climatology Network (GHCN) filtered by USDA-NASS-soybeans-production_bushels-2015)\n",
        "\n",
        "### Soybean, CBOT Soybean Futures\n",
        "- *https://blog.quandl.com/api-for-commodity-data*\n",
        "- *http://www.quandl.com/api/v3/datasets/CHRIS/CME_S1/*\n",
        "\n",
        "### Global Historical Climatology Network (GHCN)\n",
        "- *https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-ghcn*\n",
        "- *FTP: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/*\n",
        "\n",
        "\n",
        "- **TMAX** = Maximum temperature (tenths of degrees C)\n",
        "- **TMIN** = Minimum temperature (tenths of degrees C)\n",
        "- **TAVG** = Average temperature (tenths of degrees C)\n",
        "```\n",
        "(Note that TAVG from source 'S' corresponds to an average for the period ending at 2400 UTC rather than local midnight )\n",
        "```\n",
        "- **PRCP** = Precipitation (tenths of mm)\n",
        "\n",
        "### USDA-NASS-soybeans-production_bushels-2015\n",
        "- *https://usda-reports.nautilytics.com/?crop=soybeans&statistic=production_dollars&year=2007*\n",
        "- *https://www.nass.usda.gov/Data_Visualization/index.php*\n",
        "\n",
        "\n",
        "****\n",
        "\n",
        "*Based on the tutorial: https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y1pbHZNeTITl",
        "colab_type": "text"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXFIQTW4TITp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "46c7dfb0-2e49-4c8d-e369-986f12b910d8"
      },
      "source": [
        "%matplotlib inline\n",
        "import os\n",
        "from six.moves import urllib\n",
        "\n",
        "from math import sqrt\n",
        "import numpy as np\n",
        "from numpy import concatenate\n",
        "from matplotlib import pyplot as plt\n",
        "import pandas as pd\n",
        "from pandas import read_csv\n",
        "from pandas import DataFrame\n",
        "from pandas import concat\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import mean_squared_error, accuracy_score\n",
        "from sklearn import svm\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from keras.layers import LSTM, Dense, Input, TimeDistributed, Masking\n",
        "from keras.models import Model, Sequential, model_from_json\n",
        "from keras.optimizers import Adam, RMSprop\n",
        "from keras.utils import plot_model\n",
        "\n",
        "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BLrvLkEUUx9P",
        "colab_type": "text"
      },
      "source": [
        "## Defines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zk5u8eJTIUE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATASETMARLON_URL = \"https://raw.githubusercontent.com/marlonrcfranco/soyforecast/master/datasets/DatasetMarlon/datasetMarlon.csv\"\n",
        "DATASETMARLON_PATH = \"datasets/DatasetMarlon/\"\n",
        "MODEL_PATH = \"model/\"\n",
        "SETTLE_id = 5\n",
        "TARGET_NAME = 'Settle'\n",
        "\n",
        "EPOCHS = 100\n",
        "BATCH_SIZE = 77\n",
        "TRAIN_SPLIT = 0.87\n",
        "LOSS_FUNCTION = 'mse' # 'mse': Mean Square Error, 'mae': Mean Absolute Error, 'binary_crossentropy'\n",
        "LEARNING_RATE = 0.001 #0.001\n",
        "NEURONS = 128\n",
        "\n",
        "default = plt.rcParams[\"figure.figsize\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLtIadn5TIUS",
        "colab_type": "text"
      },
      "source": [
        "## Fetch Data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaE8DnIJU7et",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fetch_datasetMarlon_data(datasetmarlon_url=DATASETMARLON_URL, datasetmarlon_path=DATASETMARLON_PATH):\n",
        "    if not os.path.isdir(datasetmarlon_path):\n",
        "        os.makedirs(datasetmarlon_path)\n",
        "    csv_path = os.path.join(datasetmarlon_path, \"datasetMarlon.csv\")\n",
        "    urllib.request.urlretrieve(datasetmarlon_url, csv_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_C7xcUxVATZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fetch_datasetMarlon_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2i8i2ogF4e2",
        "colab_type": "text"
      },
      "source": [
        "## Convert series to supervised learning\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRqe54jBGxAS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# convert series to supervised learning\n",
        "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
        "\tn_vars = 1 if type(data) is list else data.shape[1]\n",
        "\tdf = DataFrame(data)\n",
        "\tcols, names = list(), list()\n",
        "\t# input sequence (t-n, ... t-1)\n",
        "\tfor i in range(n_in, 0, -1):\n",
        "\t\tcols.append(df.shift(i))\n",
        "\t\tnames += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\t# forecast sequence (t, t+1, ... t+n)\n",
        "\tfor i in range(0, n_out):\n",
        "\t\tcols.append(df.shift(-i))\n",
        "\t\tif i == 0:\n",
        "\t\t\tnames += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
        "\t\telse:\n",
        "\t\t\tnames += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
        "\t# put it all together\n",
        "\tagg = concat(cols, axis=1)\n",
        "\tagg.columns = names\n",
        "\t# drop rows with NaN values\n",
        "\tif dropnan:\n",
        "\t\tagg.dropna(inplace=True)\n",
        "\treturn agg\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H7cFH2YzVFpL",
        "colab_type": "text"
      },
      "source": [
        "## Load Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QC7ctxkoVFH3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_datasetMarlon_data(datasetmarlon_path=DATASETMARLON_PATH):\n",
        "    csv_path = os.path.join(datasetmarlon_path, \"datasetMarlon.csv\")\n",
        "    #print(csv_path)\n",
        "    return read_csv(csv_path,index_col=['Date','YEAR','MONTH','DAY'])\n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbZf-qpWVNJv",
        "colab_type": "code",
        "outputId": "0f9773e4-669b-41b5-b371-dc3747cb54a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "dataset = load_datasetMarlon_data()\n",
        "values = dataset.values\n",
        "print(dataset.shape)\n",
        "dataset.head(1)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(15062, 256)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "      <th>DE_TMAX_STD</th>\n",
              "      <th>DE_TMIN_MEAN</th>\n",
              "      <th>DE_TMIN_STD</th>\n",
              "      <th>DE_TAVG_MEAN</th>\n",
              "      <th>DE_TAVG_STD</th>\n",
              "      <th>DE_PRCP_MEAN</th>\n",
              "      <th>DE_PRCP_STD</th>\n",
              "      <th>IA_TMAX_MEAN</th>\n",
              "      <th>IA_TMAX_STD</th>\n",
              "      <th>IA_TMIN_MEAN</th>\n",
              "      <th>IA_TMIN_STD</th>\n",
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              "    </tr>\n",
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              "      <th>Date</th>\n",
              "      <th>YEAR</th>\n",
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              "      <th>2019-04-22</th>\n",
              "      <th>2019</th>\n",
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              "                            Open    High  ...  IA_PRCP_MEAN  IA_PRCP_STD\n",
              "Date       YEAR MONTH DAY                 ...                           \n",
              "2019-04-22 2019 4     22   881.5  883.25  ...           NaN          NaN\n",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3nM-9w6ciZIH",
        "colab_type": "text"
      },
      "source": [
        "## Plot heatmap for correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkMbQkiRhmlm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This can take some time\n",
        "\n",
        "#corr = dataset.corr()\n",
        "#corr.style.background_gradient(cmap='coolwarm').set_precision(2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97JhZk5EMQYV",
        "colab_type": "text"
      },
      "source": [
        "## Plot columns from CBOT dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJcnCYwfMV5n",
        "colab_type": "code",
        "outputId": "7e8e4c02-e7d0-4b25-c925-756de06c58d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "# specify columns to plot\n",
        "groups = [0, 1, 2, 3, 4, 5, 6, 7]\n",
        "i = 1\n",
        "# plot each column\n",
        "plt.figure(figsize=(10,15))\n",
        "for group in groups:\n",
        "\tplt.subplot(len(groups), 1, i)\n",
        "\tplt.plot(values[:, group])\n",
        "\tplt.title(dataset.columns[group], y=0.5, loc='right')\n",
        "\ti += 1\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+hvmiKpLJyw8QHRPLZ//u47lZWzmYmuWynjlQW/bMILfne3pYeyb9tweXdTjH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\ncqUaaUXi11xn3jX9fhQ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00YH6nF/87O8AcO+Xx5C+dBqq5SosjrmG+dP7oS0nkAmlqQCA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Em0BYQtK1hJfGo21FCaRA/RETZT0xhoT+xl1U6fkl6gCQfBOP/Fn3DLulQMS/fk+fgxBeKl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\nAwCYnIINAGByCjYAgMkp2AAAJvcBC72x0utWrgwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x1080 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0XApEK_oIVn",
        "colab_type": "text"
      },
      "source": [
        "## Plot columns from GHCN dataset (filtered by the state of Texas)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETJcEzrboIxz",
        "colab_type": "code",
        "outputId": "3464f0c8-2559-4215-d1a0-74b337d50bad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "# specify columns to plot\n",
        "groups = [8,9,10,11,12,13,14,15]\n",
        "i = 1\n",
        "# plot each column\n",
        "plt.figure(figsize=(10,15))\n",
        "for group in groups:\n",
        "\tplt.subplot(len(groups), 1, i)\n",
        "\tplt.plot(values[:, group])\n",
        "\tplt.title(dataset.columns[group], y=0.5, loc='left')\n",
        "\ti += 1\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ePkDP1OoX/hiAUyMmGu/5NBm3fObUPF5wXmHHd1F5nEFcRYG3x7l9Py8R+Br65QQj8C0V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nXX8NsbInHqIIRq1K4AwMD2dXGnwQi+IH9fwy/F0jr5UX9FM2hVV7taEMI0fmfq2lIAaFFH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\nZVF9BzLspuY3gxURMn7xv83SEo+cTOKRk9ly9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q8ikkAYtg1V3QH8CdiGE4CVAEsJ37bR\nIFBtYYg73TI9VF2Oc+YGOl4XbX3mhAwRmQ7sUNUVLRaFe9vwmwViYUZEEoFXgdmqWuq5zP0V0usv\noxWRc4Hdqro02GXpIaJwuhseVdXxwAGc7qdGYdQ2UnF+yQ8HMoAEQvOsXrcJl7bQHhG5BagFngt2\nWYJFROKBucCtwS5LKLNADHbg9G83yHTTeh0RicYJwp5T1dfc5F3uKWHcv7vddF/10hvq60TgPBHZ\ngtNNMAX4M86p8yg3j+dxNR6zu7wvUEzvqAtwfnnmq+qX7vw8nMAsHNvGGcBmVS1S1RrgNZz2Eq5t\no0Gg2sIOmrryPNNDiohcBpwLXOwGptDxuijGd7sKFSNxfrSscD9PM4FlIjKIMG0bnWGBGHwNjHav\nXonBGXA7P8hlCjh3PMJTwDpVfcBj0Xyg4aqVWcCbHumXule+TARK3K6J94GzRCTVPXtwlpsWMlT1\nZlXNVNVsnPf7Y1W9GFgEzHCztayLhjqa4eZXN32mOFfODQdG4ww2DSmqWghsF5HD3aTTgbWEYdvA\n6ZKcKCLx7v9MQ12EZdvwEJC24C4rFZGJbv1e6rGtkCAiU3GGNZynqhUei3y9516/Y9x24qtdhQRV\nXaWqA1Q12/08zce5KKyQMGwbndbdVwOEwgvn6o5vca5suSXY5emmYzwJpzthJbDcfZ2DM07hI+A7\n4EOgn5tfgEfcOlkF5Hhs63Kcgah5wM+DfWxdrJfTaLpqcgTOB2ce8AoQ66b3cefz3OUjPNa/xa2j\nDYTwFT7AOCDXbR9v4FzNFJZtA7gdWA+sBv6OcxVc2LQN4AWc8XE1OF+sVwSyLQA5bt1uBB7GvbF4\nT3z5qIs8nDFODZ+jj7X3nuPjO8ZXu+qpL2/10WL5FpqumuzVbSOQL7uzvjHGGGNMkFjXpDHGGGNM\nkFggZowxxhgTJBaIGWOMMcYEiQVixhhjjDFBYoGYMcYYY0yQWCBmjDHGGBMkFogZY4wxxgSJBWLG\nGGOMMUHy/zv+UUHjR/u1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x1080 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EY6ofb_5G4jb",
        "colab_type": "text"
      },
      "source": [
        "## Integer encode direction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0Ac1I9EHW2a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoder = LabelEncoder()\n",
        "values[:,4] = encoder.fit_transform(values[:,4])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_S92IP9HbiM",
        "colab_type": "text"
      },
      "source": [
        "## Ensure all data is float\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgD61EgpHddz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "values = values.astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcngVhupHjnq",
        "colab_type": "text"
      },
      "source": [
        "## Normalize features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njORF0_uHj9z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "scaled = scaler.fit_transform(values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vYGQ-TLOHtMu",
        "colab_type": "text"
      },
      "source": [
        "## Specify the number of lag hours"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zk3q5RmzHtjA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_days = 7 # A month before\n",
        "n_features = 256"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2l3s1TxWH0Fo",
        "colab_type": "text"
      },
      "source": [
        "## Frame as supervised learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNTDBCL7H0uR",
        "colab_type": "code",
        "outputId": "a6e7d3c2-777e-43db-ffda-4f95b76e5169",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "reframed = series_to_supervised(scaled, n_days, 1)\n",
        "print(reframed.shape)\n",
        "reframed.head()\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10991, 2048)\n"
          ],
          "name": "stdout"
        },
        {
          "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",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>var1(t-7)</th>\n",
              "      <th>var2(t-7)</th>\n",
              "      <th>var3(t-7)</th>\n",
              "      <th>var4(t-7)</th>\n",
              "      <th>var5(t-7)</th>\n",
              "      <th>var6(t-7)</th>\n",
              "      <th>var7(t-7)</th>\n",
              "      <th>var8(t-7)</th>\n",
              "      <th>var9(t-7)</th>\n",
              "      <th>var10(t-7)</th>\n",
              "      <th>var11(t-7)</th>\n",
              "      <th>var12(t-7)</th>\n",
              "      <th>var13(t-7)</th>\n",
              "      <th>var14(t-7)</th>\n",
              "      <th>var15(t-7)</th>\n",
              "      <th>var16(t-7)</th>\n",
              "      <th>var17(t-7)</th>\n",
              "      <th>var18(t-7)</th>\n",
              "      <th>var19(t-7)</th>\n",
              "      <th>var20(t-7)</th>\n",
              "      <th>var21(t-7)</th>\n",
              "      <th>var22(t-7)</th>\n",
              "      <th>var23(t-7)</th>\n",
              "      <th>var24(t-7)</th>\n",
              "      <th>var25(t-7)</th>\n",
              "      <th>var26(t-7)</th>\n",
              "      <th>var27(t-7)</th>\n",
              "      <th>var28(t-7)</th>\n",
              "      <th>var29(t-7)</th>\n",
              "      <th>var30(t-7)</th>\n",
              "      <th>var31(t-7)</th>\n",
              "      <th>var32(t-7)</th>\n",
              "      <th>var33(t-7)</th>\n",
              "      <th>var34(t-7)</th>\n",
              "      <th>var35(t-7)</th>\n",
              "      <th>var36(t-7)</th>\n",
              "      <th>var37(t-7)</th>\n",
              "      <th>var38(t-7)</th>\n",
              "      <th>var39(t-7)</th>\n",
              "      <th>var40(t-7)</th>\n",
              "      <th>...</th>\n",
              "      <th>var217(t)</th>\n",
              "      <th>var218(t)</th>\n",
              "      <th>var219(t)</th>\n",
              "      <th>var220(t)</th>\n",
              "      <th>var221(t)</th>\n",
              "      <th>var222(t)</th>\n",
              "      <th>var223(t)</th>\n",
              "      <th>var224(t)</th>\n",
              "      <th>var225(t)</th>\n",
              "      <th>var226(t)</th>\n",
              "      <th>var227(t)</th>\n",
              "      <th>var228(t)</th>\n",
              "      <th>var229(t)</th>\n",
              "      <th>var230(t)</th>\n",
              "      <th>var231(t)</th>\n",
              "      <th>var232(t)</th>\n",
              "      <th>var233(t)</th>\n",
              "      <th>var234(t)</th>\n",
              "      <th>var235(t)</th>\n",
              "      <th>var236(t)</th>\n",
              "      <th>var237(t)</th>\n",
              "      <th>var238(t)</th>\n",
              "      <th>var239(t)</th>\n",
              "      <th>var240(t)</th>\n",
              "      <th>var241(t)</th>\n",
              "      <th>var242(t)</th>\n",
              "      <th>var243(t)</th>\n",
              "      <th>var244(t)</th>\n",
              "      <th>var245(t)</th>\n",
              "      <th>var246(t)</th>\n",
              "      <th>var247(t)</th>\n",
              "      <th>var248(t)</th>\n",
              "      <th>var249(t)</th>\n",
              "      <th>var250(t)</th>\n",
              "      <th>var251(t)</th>\n",
              "      <th>var252(t)</th>\n",
              "      <th>var253(t)</th>\n",
              "      <th>var254(t)</th>\n",
              "      <th>var255(t)</th>\n",
              "      <th>var256(t)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.430163</td>\n",
              "      <td>0.431369</td>\n",
              "      <td>0.429128</td>\n",
              "      <td>0.431731</td>\n",
              "      <td>0.000359</td>\n",
              "      <td>0.431572</td>\n",
              "      <td>0.171927</td>\n",
              "      <td>0.353242</td>\n",
              "      <td>0.201763</td>\n",
              "      <td>0.431541</td>\n",
              "      <td>0.456176</td>\n",
              "      <td>0.300841</td>\n",
              "      <td>0.169949</td>\n",
              "      <td>0.328450</td>\n",
              "      <td>0.896115</td>\n",
              "      <td>0.851300</td>\n",
              "      <td>0.229714</td>\n",
              "      <td>0.547190</td>\n",
              "      <td>0.458680</td>\n",
              "      <td>0.379904</td>\n",
              "      <td>0.277167</td>\n",
              "      <td>0.401238</td>\n",
              "      <td>0.000586</td>\n",
              "      <td>0.015170</td>\n",
              "      <td>0.404762</td>\n",
              "      <td>0.356728</td>\n",
              "      <td>0.569178</td>\n",
              "      <td>0.090203</td>\n",
              "      <td>0.401204</td>\n",
              "      <td>0.168394</td>\n",
              "      <td>0.019776</td>\n",
              "      <td>0.150741</td>\n",
              "      <td>0.363888</td>\n",
              "      <td>0.365142</td>\n",
              "      <td>0.557153</td>\n",
              "      <td>0.075924</td>\n",
              "      <td>0.360311</td>\n",
              "      <td>0.185684</td>\n",
              "      <td>0.062892</td>\n",
              "      <td>0.409582</td>\n",
              "      <td>...</td>\n",
              "      <td>0.371910</td>\n",
              "      <td>0.524527</td>\n",
              "      <td>0.588045</td>\n",
              "      <td>0.493318</td>\n",
              "      <td>0.460136</td>\n",
              "      <td>0.560212</td>\n",
              "      <td>0.317801</td>\n",
              "      <td>0.402935</td>\n",
              "      <td>0.487779</td>\n",
              "      <td>0.293198</td>\n",
              "      <td>0.585826</td>\n",
              "      <td>0.106894</td>\n",
              "      <td>0.511277</td>\n",
              "      <td>0.111702</td>\n",
              "      <td>0.000727</td>\n",
              "      <td>0.004247</td>\n",
              "      <td>0.532091</td>\n",
              "      <td>0.444306</td>\n",
              "      <td>0.648556</td>\n",
              "      <td>0.320789</td>\n",
              "      <td>0.617937</td>\n",
              "      <td>0.500162</td>\n",
              "      <td>0.244014</td>\n",
              "      <td>0.333859</td>\n",
              "      <td>0.250646</td>\n",
              "      <td>0.281392</td>\n",
              "      <td>0.446682</td>\n",
              "      <td>0.271970</td>\n",
              "      <td>0.247504</td>\n",
              "      <td>0.203999</td>\n",
              "      <td>0.055373</td>\n",
              "      <td>0.176851</td>\n",
              "      <td>0.512180</td>\n",
              "      <td>0.490143</td>\n",
              "      <td>0.616917</td>\n",
              "      <td>0.128333</td>\n",
              "      <td>0.470187</td>\n",
              "      <td>0.265425</td>\n",
              "      <td>0.000550</td>\n",
              "      <td>0.006923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.435911</td>\n",
              "      <td>0.436790</td>\n",
              "      <td>0.430404</td>\n",
              "      <td>0.430614</td>\n",
              "      <td>0.002942</td>\n",
              "      <td>0.430614</td>\n",
              "      <td>0.242938</td>\n",
              "      <td>0.361835</td>\n",
              "      <td>0.230660</td>\n",
              "      <td>0.479189</td>\n",
              "      <td>0.494639</td>\n",
              "      <td>0.463921</td>\n",
              "      <td>0.226693</td>\n",
              "      <td>0.421765</td>\n",
              "      <td>0.006275</td>\n",
              "      <td>0.031552</td>\n",
              "      <td>0.214966</td>\n",
              "      <td>0.502637</td>\n",
              "      <td>0.431140</td>\n",
              "      <td>0.209312</td>\n",
              "      <td>0.265397</td>\n",
              "      <td>0.359200</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.001789</td>\n",
              "      <td>0.423053</td>\n",
              "      <td>0.431792</td>\n",
              "      <td>0.582858</td>\n",
              "      <td>0.157163</td>\n",
              "      <td>0.432913</td>\n",
              "      <td>0.283714</td>\n",
              "      <td>0.013212</td>\n",
              "      <td>0.098600</td>\n",
              "      <td>0.396977</td>\n",
              "      <td>0.440580</td>\n",
              "      <td>0.590694</td>\n",
              "      <td>0.143208</td>\n",
              "      <td>0.396221</td>\n",
              "      <td>0.309503</td>\n",
              "      <td>0.000522</td>\n",
              "      <td>0.024596</td>\n",
              "      <td>...</td>\n",
              "      <td>0.355455</td>\n",
              "      <td>0.528713</td>\n",
              "      <td>0.596836</td>\n",
              "      <td>0.557724</td>\n",
              "      <td>0.453071</td>\n",
              "      <td>0.551214</td>\n",
              "      <td>0.820306</td>\n",
              "      <td>0.644928</td>\n",
              "      <td>0.544200</td>\n",
              "      <td>0.412315</td>\n",
              "      <td>0.642775</td>\n",
              "      <td>0.096670</td>\n",
              "      <td>0.558766</td>\n",
              "      <td>0.337722</td>\n",
              "      <td>0.007512</td>\n",
              "      <td>0.018592</td>\n",
              "      <td>0.572613</td>\n",
              "      <td>0.546953</td>\n",
              "      <td>0.606333</td>\n",
              "      <td>0.295206</td>\n",
              "      <td>0.634065</td>\n",
              "      <td>0.533847</td>\n",
              "      <td>0.324920</td>\n",
              "      <td>0.375600</td>\n",
              "      <td>0.253182</td>\n",
              "      <td>0.353047</td>\n",
              "      <td>0.445714</td>\n",
              "      <td>0.250457</td>\n",
              "      <td>0.246859</td>\n",
              "      <td>0.204921</td>\n",
              "      <td>0.017327</td>\n",
              "      <td>0.068066</td>\n",
              "      <td>0.502198</td>\n",
              "      <td>0.469681</td>\n",
              "      <td>0.620976</td>\n",
              "      <td>0.137802</td>\n",
              "      <td>0.477043</td>\n",
              "      <td>0.300327</td>\n",
              "      <td>0.036553</td>\n",
              "      <td>0.085632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.442617</td>\n",
              "      <td>0.442212</td>\n",
              "      <td>0.435350</td>\n",
              "      <td>0.436359</td>\n",
              "      <td>0.003516</td>\n",
              "      <td>0.436359</td>\n",
              "      <td>0.251458</td>\n",
              "      <td>0.365132</td>\n",
              "      <td>0.242662</td>\n",
              "      <td>0.496247</td>\n",
              "      <td>0.468674</td>\n",
              "      <td>0.358339</td>\n",
              "      <td>0.222838</td>\n",
              "      <td>0.404125</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.001702</td>\n",
              "      <td>0.190328</td>\n",
              "      <td>0.414317</td>\n",
              "      <td>0.415874</td>\n",
              "      <td>0.125339</td>\n",
              "      <td>0.238111</td>\n",
              "      <td>0.262694</td>\n",
              "      <td>0.002535</td>\n",
              "      <td>0.016891</td>\n",
              "      <td>0.453810</td>\n",
              "      <td>0.505408</td>\n",
              "      <td>0.574211</td>\n",
              "      <td>0.109289</td>\n",
              "      <td>0.443341</td>\n",
              "      <td>0.286492</td>\n",
              "      <td>0.000728</td>\n",
              "      <td>0.006846</td>\n",
              "      <td>0.395761</td>\n",
              "      <td>0.444830</td>\n",
              "      <td>0.570300</td>\n",
              "      <td>0.113684</td>\n",
              "      <td>0.396403</td>\n",
              "      <td>0.309299</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.302206</td>\n",
              "      <td>0.446837</td>\n",
              "      <td>0.550406</td>\n",
              "      <td>0.448382</td>\n",
              "      <td>0.412175</td>\n",
              "      <td>0.482795</td>\n",
              "      <td>0.605323</td>\n",
              "      <td>0.635286</td>\n",
              "      <td>0.483573</td>\n",
              "      <td>0.311606</td>\n",
              "      <td>0.614646</td>\n",
              "      <td>0.060367</td>\n",
              "      <td>0.531559</td>\n",
              "      <td>0.190482</td>\n",
              "      <td>0.004040</td>\n",
              "      <td>0.011751</td>\n",
              "      <td>0.513266</td>\n",
              "      <td>0.440836</td>\n",
              "      <td>0.539564</td>\n",
              "      <td>0.174681</td>\n",
              "      <td>0.607398</td>\n",
              "      <td>0.449416</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>0.159672</td>\n",
              "      <td>0.244949</td>\n",
              "      <td>0.153596</td>\n",
              "      <td>0.441872</td>\n",
              "      <td>0.119184</td>\n",
              "      <td>0.238309</td>\n",
              "      <td>0.096205</td>\n",
              "      <td>0.001507</td>\n",
              "      <td>0.026990</td>\n",
              "      <td>0.425961</td>\n",
              "      <td>0.281899</td>\n",
              "      <td>0.615545</td>\n",
              "      <td>0.133919</td>\n",
              "      <td>0.452272</td>\n",
              "      <td>0.191550</td>\n",
              "      <td>0.024786</td>\n",
              "      <td>0.053430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.440542</td>\n",
              "      <td>0.444126</td>\n",
              "      <td>0.440775</td>\n",
              "      <td>0.443221</td>\n",
              "      <td>0.001148</td>\n",
              "      <td>0.443221</td>\n",
              "      <td>0.246762</td>\n",
              "      <td>0.382209</td>\n",
              "      <td>0.219540</td>\n",
              "      <td>0.455343</td>\n",
              "      <td>0.418607</td>\n",
              "      <td>0.180602</td>\n",
              "      <td>0.205872</td>\n",
              "      <td>0.364898</td>\n",
              "      <td>0.000488</td>\n",
              "      <td>0.003224</td>\n",
              "      <td>0.195655</td>\n",
              "      <td>0.436074</td>\n",
              "      <td>0.447489</td>\n",
              "      <td>0.354754</td>\n",
              "      <td>0.239016</td>\n",
              "      <td>0.299704</td>\n",
              "      <td>0.209493</td>\n",
              "      <td>0.329362</td>\n",
              "      <td>0.424834</td>\n",
              "      <td>0.395336</td>\n",
              "      <td>0.552077</td>\n",
              "      <td>0.108095</td>\n",
              "      <td>0.437964</td>\n",
              "      <td>0.277871</td>\n",
              "      <td>0.000249</td>\n",
              "      <td>0.003263</td>\n",
              "      <td>0.363192</td>\n",
              "      <td>0.358510</td>\n",
              "      <td>0.531583</td>\n",
              "      <td>0.050569</td>\n",
              "      <td>0.383882</td>\n",
              "      <td>0.260925</td>\n",
              "      <td>0.000385</td>\n",
              "      <td>0.016370</td>\n",
              "      <td>...</td>\n",
              "      <td>0.308864</td>\n",
              "      <td>0.406368</td>\n",
              "      <td>0.482938</td>\n",
              "      <td>0.349272</td>\n",
              "      <td>0.389979</td>\n",
              "      <td>0.398535</td>\n",
              "      <td>0.051966</td>\n",
              "      <td>0.127961</td>\n",
              "      <td>0.478306</td>\n",
              "      <td>0.342071</td>\n",
              "      <td>0.583938</td>\n",
              "      <td>0.132277</td>\n",
              "      <td>0.532925</td>\n",
              "      <td>0.222117</td>\n",
              "      <td>0.001670</td>\n",
              "      <td>0.006387</td>\n",
              "      <td>0.504641</td>\n",
              "      <td>0.396672</td>\n",
              "      <td>0.482767</td>\n",
              "      <td>0.095158</td>\n",
              "      <td>0.572430</td>\n",
              "      <td>0.422047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.249714</td>\n",
              "      <td>0.256804</td>\n",
              "      <td>0.441274</td>\n",
              "      <td>0.099619</td>\n",
              "      <td>0.243130</td>\n",
              "      <td>0.150464</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.420924</td>\n",
              "      <td>0.232492</td>\n",
              "      <td>0.589131</td>\n",
              "      <td>0.153001</td>\n",
              "      <td>0.443218</td>\n",
              "      <td>0.145499</td>\n",
              "      <td>0.070249</td>\n",
              "      <td>0.187655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.440542</td>\n",
              "      <td>0.441893</td>\n",
              "      <td>0.440137</td>\n",
              "      <td>0.440508</td>\n",
              "      <td>0.669824</td>\n",
              "      <td>0.440987</td>\n",
              "      <td>0.187976</td>\n",
              "      <td>0.398964</td>\n",
              "      <td>0.202366</td>\n",
              "      <td>0.434235</td>\n",
              "      <td>0.425404</td>\n",
              "      <td>0.267301</td>\n",
              "      <td>0.176228</td>\n",
              "      <td>0.318783</td>\n",
              "      <td>0.004867</td>\n",
              "      <td>0.024482</td>\n",
              "      <td>0.206077</td>\n",
              "      <td>0.475031</td>\n",
              "      <td>0.462841</td>\n",
              "      <td>0.424089</td>\n",
              "      <td>0.261478</td>\n",
              "      <td>0.368925</td>\n",
              "      <td>0.063191</td>\n",
              "      <td>0.131305</td>\n",
              "      <td>0.325204</td>\n",
              "      <td>0.083416</td>\n",
              "      <td>0.515858</td>\n",
              "      <td>0.168162</td>\n",
              "      <td>0.352007</td>\n",
              "      <td>0.054233</td>\n",
              "      <td>0.025444</td>\n",
              "      <td>0.143774</td>\n",
              "      <td>0.291209</td>\n",
              "      <td>0.168674</td>\n",
              "      <td>0.497531</td>\n",
              "      <td>0.027886</td>\n",
              "      <td>0.332575</td>\n",
              "      <td>0.072814</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.021278</td>\n",
              "      <td>...</td>\n",
              "      <td>0.351615</td>\n",
              "      <td>0.449122</td>\n",
              "      <td>0.416758</td>\n",
              "      <td>0.137755</td>\n",
              "      <td>0.394112</td>\n",
              "      <td>0.405500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.456633</td>\n",
              "      <td>0.193639</td>\n",
              "      <td>0.544582</td>\n",
              "      <td>0.168995</td>\n",
              "      <td>0.508103</td>\n",
              "      <td>0.062449</td>\n",
              "      <td>0.003539</td>\n",
              "      <td>0.010885</td>\n",
              "      <td>0.457189</td>\n",
              "      <td>0.299672</td>\n",
              "      <td>0.460608</td>\n",
              "      <td>0.105253</td>\n",
              "      <td>0.517366</td>\n",
              "      <td>0.331736</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.249232</td>\n",
              "      <td>0.256895</td>\n",
              "      <td>0.439774</td>\n",
              "      <td>0.060329</td>\n",
              "      <td>0.239830</td>\n",
              "      <td>0.117333</td>\n",
              "      <td>0.002638</td>\n",
              "      <td>0.021441</td>\n",
              "      <td>0.427566</td>\n",
              "      <td>0.225106</td>\n",
              "      <td>0.564925</td>\n",
              "      <td>0.079126</td>\n",
              "      <td>0.437787</td>\n",
              "      <td>0.116188</td>\n",
              "      <td>0.008915</td>\n",
              "      <td>0.031045</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2048 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    var1(t-7)  var2(t-7)  var3(t-7)  ...  var254(t)  var255(t)  var256(t)\n",
              "8    0.430163   0.431369   0.429128  ...   0.265425   0.000550   0.006923\n",
              "9    0.435911   0.436790   0.430404  ...   0.300327   0.036553   0.085632\n",
              "10   0.442617   0.442212   0.435350  ...   0.191550   0.024786   0.053430\n",
              "11   0.440542   0.444126   0.440775  ...   0.145499   0.070249   0.187655\n",
              "12   0.440542   0.441893   0.440137  ...   0.116188   0.008915   0.031045\n",
              "\n",
              "[5 rows x 2048 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_K1my4wNBon",
        "colab_type": "text"
      },
      "source": [
        "## Drop columns we don't want to predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUXtuD1dNEvR",
        "colab_type": "code",
        "outputId": "b60de0f1-3362-4d25-c1ab-9c0849478289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "cols = [reframed.columns.get_loc(col) for col in reframed.columns if reframed.columns.get_loc(col)>=n_days*n_features and reframed.columns.get_loc(col)!=(n_days*n_features+SETTLE_id)]\n",
        "reframed.drop(reframed.columns[cols], axis=1, inplace=True)\n",
        "print(reframed.shape)\n",
        "reframed.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10991, 1793)\n"
          ],
          "name": "stdout"
        },
        {
          "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>var1(t-7)</th>\n",
              "      <th>var2(t-7)</th>\n",
              "      <th>var3(t-7)</th>\n",
              "      <th>var4(t-7)</th>\n",
              "      <th>var5(t-7)</th>\n",
              "      <th>var6(t-7)</th>\n",
              "      <th>var7(t-7)</th>\n",
              "      <th>var8(t-7)</th>\n",
              "      <th>var9(t-7)</th>\n",
              "      <th>var10(t-7)</th>\n",
              "      <th>var11(t-7)</th>\n",
              "      <th>var12(t-7)</th>\n",
              "      <th>var13(t-7)</th>\n",
              "      <th>var14(t-7)</th>\n",
              "      <th>var15(t-7)</th>\n",
              "      <th>var16(t-7)</th>\n",
              "      <th>var17(t-7)</th>\n",
              "      <th>var18(t-7)</th>\n",
              "      <th>var19(t-7)</th>\n",
              "      <th>var20(t-7)</th>\n",
              "      <th>var21(t-7)</th>\n",
              "      <th>var22(t-7)</th>\n",
              "      <th>var23(t-7)</th>\n",
              "      <th>var24(t-7)</th>\n",
              "      <th>var25(t-7)</th>\n",
              "      <th>var26(t-7)</th>\n",
              "      <th>var27(t-7)</th>\n",
              "      <th>var28(t-7)</th>\n",
              "      <th>var29(t-7)</th>\n",
              "      <th>var30(t-7)</th>\n",
              "      <th>var31(t-7)</th>\n",
              "      <th>var32(t-7)</th>\n",
              "      <th>var33(t-7)</th>\n",
              "      <th>var34(t-7)</th>\n",
              "      <th>var35(t-7)</th>\n",
              "      <th>var36(t-7)</th>\n",
              "      <th>var37(t-7)</th>\n",
              "      <th>var38(t-7)</th>\n",
              "      <th>var39(t-7)</th>\n",
              "      <th>var40(t-7)</th>\n",
              "      <th>...</th>\n",
              "      <th>var218(t-1)</th>\n",
              "      <th>var219(t-1)</th>\n",
              "      <th>var220(t-1)</th>\n",
              "      <th>var221(t-1)</th>\n",
              "      <th>var222(t-1)</th>\n",
              "      <th>var223(t-1)</th>\n",
              "      <th>var224(t-1)</th>\n",
              "      <th>var225(t-1)</th>\n",
              "      <th>var226(t-1)</th>\n",
              "      <th>var227(t-1)</th>\n",
              "      <th>var228(t-1)</th>\n",
              "      <th>var229(t-1)</th>\n",
              "      <th>var230(t-1)</th>\n",
              "      <th>var231(t-1)</th>\n",
              "      <th>var232(t-1)</th>\n",
              "      <th>var233(t-1)</th>\n",
              "      <th>var234(t-1)</th>\n",
              "      <th>var235(t-1)</th>\n",
              "      <th>var236(t-1)</th>\n",
              "      <th>var237(t-1)</th>\n",
              "      <th>var238(t-1)</th>\n",
              "      <th>var239(t-1)</th>\n",
              "      <th>var240(t-1)</th>\n",
              "      <th>var241(t-1)</th>\n",
              "      <th>var242(t-1)</th>\n",
              "      <th>var243(t-1)</th>\n",
              "      <th>var244(t-1)</th>\n",
              "      <th>var245(t-1)</th>\n",
              "      <th>var246(t-1)</th>\n",
              "      <th>var247(t-1)</th>\n",
              "      <th>var248(t-1)</th>\n",
              "      <th>var249(t-1)</th>\n",
              "      <th>var250(t-1)</th>\n",
              "      <th>var251(t-1)</th>\n",
              "      <th>var252(t-1)</th>\n",
              "      <th>var253(t-1)</th>\n",
              "      <th>var254(t-1)</th>\n",
              "      <th>var255(t-1)</th>\n",
              "      <th>var256(t-1)</th>\n",
              "      <th>var6(t)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.430163</td>\n",
              "      <td>0.431369</td>\n",
              "      <td>0.429128</td>\n",
              "      <td>0.431731</td>\n",
              "      <td>0.000359</td>\n",
              "      <td>0.431572</td>\n",
              "      <td>0.171927</td>\n",
              "      <td>0.353242</td>\n",
              "      <td>0.201763</td>\n",
              "      <td>0.431541</td>\n",
              "      <td>0.456176</td>\n",
              "      <td>0.300841</td>\n",
              "      <td>0.169949</td>\n",
              "      <td>0.328450</td>\n",
              "      <td>0.896115</td>\n",
              "      <td>0.851300</td>\n",
              "      <td>0.229714</td>\n",
              "      <td>0.547190</td>\n",
              "      <td>0.458680</td>\n",
              "      <td>0.379904</td>\n",
              "      <td>0.277167</td>\n",
              "      <td>0.401238</td>\n",
              "      <td>0.000586</td>\n",
              "      <td>0.015170</td>\n",
              "      <td>0.404762</td>\n",
              "      <td>0.356728</td>\n",
              "      <td>0.569178</td>\n",
              "      <td>0.090203</td>\n",
              "      <td>0.401204</td>\n",
              "      <td>0.168394</td>\n",
              "      <td>0.019776</td>\n",
              "      <td>0.150741</td>\n",
              "      <td>0.363888</td>\n",
              "      <td>0.365142</td>\n",
              "      <td>0.557153</td>\n",
              "      <td>0.075924</td>\n",
              "      <td>0.360311</td>\n",
              "      <td>0.185684</td>\n",
              "      <td>0.062892</td>\n",
              "      <td>0.409582</td>\n",
              "      <td>...</td>\n",
              "      <td>0.577949</td>\n",
              "      <td>0.554269</td>\n",
              "      <td>0.395771</td>\n",
              "      <td>0.503953</td>\n",
              "      <td>0.607306</td>\n",
              "      <td>0.040479</td>\n",
              "      <td>0.193762</td>\n",
              "      <td>0.415522</td>\n",
              "      <td>0.139545</td>\n",
              "      <td>0.553601</td>\n",
              "      <td>0.140833</td>\n",
              "      <td>0.489100</td>\n",
              "      <td>0.095630</td>\n",
              "      <td>0.014085</td>\n",
              "      <td>0.083460</td>\n",
              "      <td>0.558218</td>\n",
              "      <td>0.494523</td>\n",
              "      <td>0.533181</td>\n",
              "      <td>0.096990</td>\n",
              "      <td>0.608214</td>\n",
              "      <td>0.463769</td>\n",
              "      <td>0.001140</td>\n",
              "      <td>0.028621</td>\n",
              "      <td>0.250913</td>\n",
              "      <td>0.275345</td>\n",
              "      <td>0.442415</td>\n",
              "      <td>0.128911</td>\n",
              "      <td>0.245567</td>\n",
              "      <td>0.173112</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0.004264</td>\n",
              "      <td>0.462003</td>\n",
              "      <td>0.397564</td>\n",
              "      <td>0.608633</td>\n",
              "      <td>0.127016</td>\n",
              "      <td>0.442540</td>\n",
              "      <td>0.142258</td>\n",
              "      <td>0.037625</td>\n",
              "      <td>0.254125</td>\n",
              "      <td>0.443221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.435911</td>\n",
              "      <td>0.436790</td>\n",
              "      <td>0.430404</td>\n",
              "      <td>0.430614</td>\n",
              "      <td>0.002942</td>\n",
              "      <td>0.430614</td>\n",
              "      <td>0.242938</td>\n",
              "      <td>0.361835</td>\n",
              "      <td>0.230660</td>\n",
              "      <td>0.479189</td>\n",
              "      <td>0.494639</td>\n",
              "      <td>0.463921</td>\n",
              "      <td>0.226693</td>\n",
              "      <td>0.421765</td>\n",
              "      <td>0.006275</td>\n",
              "      <td>0.031552</td>\n",
              "      <td>0.214966</td>\n",
              "      <td>0.502637</td>\n",
              "      <td>0.431140</td>\n",
              "      <td>0.209312</td>\n",
              "      <td>0.265397</td>\n",
              "      <td>0.359200</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.001789</td>\n",
              "      <td>0.423053</td>\n",
              "      <td>0.431792</td>\n",
              "      <td>0.582858</td>\n",
              "      <td>0.157163</td>\n",
              "      <td>0.432913</td>\n",
              "      <td>0.283714</td>\n",
              "      <td>0.013212</td>\n",
              "      <td>0.098600</td>\n",
              "      <td>0.396977</td>\n",
              "      <td>0.440580</td>\n",
              "      <td>0.590694</td>\n",
              "      <td>0.143208</td>\n",
              "      <td>0.396221</td>\n",
              "      <td>0.309503</td>\n",
              "      <td>0.000522</td>\n",
              "      <td>0.024596</td>\n",
              "      <td>...</td>\n",
              "      <td>0.524527</td>\n",
              "      <td>0.588045</td>\n",
              "      <td>0.493318</td>\n",
              "      <td>0.460136</td>\n",
              "      <td>0.560212</td>\n",
              "      <td>0.317801</td>\n",
              "      <td>0.402935</td>\n",
              "      <td>0.487779</td>\n",
              "      <td>0.293198</td>\n",
              "      <td>0.585826</td>\n",
              "      <td>0.106894</td>\n",
              "      <td>0.511277</td>\n",
              "      <td>0.111702</td>\n",
              "      <td>0.000727</td>\n",
              "      <td>0.004247</td>\n",
              "      <td>0.532091</td>\n",
              "      <td>0.444306</td>\n",
              "      <td>0.648556</td>\n",
              "      <td>0.320789</td>\n",
              "      <td>0.617937</td>\n",
              "      <td>0.500162</td>\n",
              "      <td>0.244014</td>\n",
              "      <td>0.333859</td>\n",
              "      <td>0.250646</td>\n",
              "      <td>0.281392</td>\n",
              "      <td>0.446682</td>\n",
              "      <td>0.271970</td>\n",
              "      <td>0.247504</td>\n",
              "      <td>0.203999</td>\n",
              "      <td>0.055373</td>\n",
              "      <td>0.176851</td>\n",
              "      <td>0.512180</td>\n",
              "      <td>0.490143</td>\n",
              "      <td>0.616917</td>\n",
              "      <td>0.128333</td>\n",
              "      <td>0.470187</td>\n",
              "      <td>0.265425</td>\n",
              "      <td>0.000550</td>\n",
              "      <td>0.006923</td>\n",
              "      <td>0.443221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.442617</td>\n",
              "      <td>0.442212</td>\n",
              "      <td>0.435350</td>\n",
              "      <td>0.436359</td>\n",
              "      <td>0.003516</td>\n",
              "      <td>0.436359</td>\n",
              "      <td>0.251458</td>\n",
              "      <td>0.365132</td>\n",
              "      <td>0.242662</td>\n",
              "      <td>0.496247</td>\n",
              "      <td>0.468674</td>\n",
              "      <td>0.358339</td>\n",
              "      <td>0.222838</td>\n",
              "      <td>0.404125</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>0.001702</td>\n",
              "      <td>0.190328</td>\n",
              "      <td>0.414317</td>\n",
              "      <td>0.415874</td>\n",
              "      <td>0.125339</td>\n",
              "      <td>0.238111</td>\n",
              "      <td>0.262694</td>\n",
              "      <td>0.002535</td>\n",
              "      <td>0.016891</td>\n",
              "      <td>0.453810</td>\n",
              "      <td>0.505408</td>\n",
              "      <td>0.574211</td>\n",
              "      <td>0.109289</td>\n",
              "      <td>0.443341</td>\n",
              "      <td>0.286492</td>\n",
              "      <td>0.000728</td>\n",
              "      <td>0.006846</td>\n",
              "      <td>0.395761</td>\n",
              "      <td>0.444830</td>\n",
              "      <td>0.570300</td>\n",
              "      <td>0.113684</td>\n",
              "      <td>0.396403</td>\n",
              "      <td>0.309299</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.528713</td>\n",
              "      <td>0.596836</td>\n",
              "      <td>0.557724</td>\n",
              "      <td>0.453071</td>\n",
              "      <td>0.551214</td>\n",
              "      <td>0.820306</td>\n",
              "      <td>0.644928</td>\n",
              "      <td>0.544200</td>\n",
              "      <td>0.412315</td>\n",
              "      <td>0.642775</td>\n",
              "      <td>0.096670</td>\n",
              "      <td>0.558766</td>\n",
              "      <td>0.337722</td>\n",
              "      <td>0.007512</td>\n",
              "      <td>0.018592</td>\n",
              "      <td>0.572613</td>\n",
              "      <td>0.546953</td>\n",
              "      <td>0.606333</td>\n",
              "      <td>0.295206</td>\n",
              "      <td>0.634065</td>\n",
              "      <td>0.533847</td>\n",
              "      <td>0.324920</td>\n",
              "      <td>0.375600</td>\n",
              "      <td>0.253182</td>\n",
              "      <td>0.353047</td>\n",
              "      <td>0.445714</td>\n",
              "      <td>0.250457</td>\n",
              "      <td>0.246859</td>\n",
              "      <td>0.204921</td>\n",
              "      <td>0.017327</td>\n",
              "      <td>0.068066</td>\n",
              "      <td>0.502198</td>\n",
              "      <td>0.469681</td>\n",
              "      <td>0.620976</td>\n",
              "      <td>0.137802</td>\n",
              "      <td>0.477043</td>\n",
              "      <td>0.300327</td>\n",
              "      <td>0.036553</td>\n",
              "      <td>0.085632</td>\n",
              "      <td>0.443381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.440542</td>\n",
              "      <td>0.444126</td>\n",
              "      <td>0.440775</td>\n",
              "      <td>0.443221</td>\n",
              "      <td>0.001148</td>\n",
              "      <td>0.443221</td>\n",
              "      <td>0.246762</td>\n",
              "      <td>0.382209</td>\n",
              "      <td>0.219540</td>\n",
              "      <td>0.455343</td>\n",
              "      <td>0.418607</td>\n",
              "      <td>0.180602</td>\n",
              "      <td>0.205872</td>\n",
              "      <td>0.364898</td>\n",
              "      <td>0.000488</td>\n",
              "      <td>0.003224</td>\n",
              "      <td>0.195655</td>\n",
              "      <td>0.436074</td>\n",
              "      <td>0.447489</td>\n",
              "      <td>0.354754</td>\n",
              "      <td>0.239016</td>\n",
              "      <td>0.299704</td>\n",
              "      <td>0.209493</td>\n",
              "      <td>0.329362</td>\n",
              "      <td>0.424834</td>\n",
              "      <td>0.395336</td>\n",
              "      <td>0.552077</td>\n",
              "      <td>0.108095</td>\n",
              "      <td>0.437964</td>\n",
              "      <td>0.277871</td>\n",
              "      <td>0.000249</td>\n",
              "      <td>0.003263</td>\n",
              "      <td>0.363192</td>\n",
              "      <td>0.358510</td>\n",
              "      <td>0.531583</td>\n",
              "      <td>0.050569</td>\n",
              "      <td>0.383882</td>\n",
              "      <td>0.260925</td>\n",
              "      <td>0.000385</td>\n",
              "      <td>0.016370</td>\n",
              "      <td>...</td>\n",
              "      <td>0.446837</td>\n",
              "      <td>0.550406</td>\n",
              "      <td>0.448382</td>\n",
              "      <td>0.412175</td>\n",
              "      <td>0.482795</td>\n",
              "      <td>0.605323</td>\n",
              "      <td>0.635286</td>\n",
              "      <td>0.483573</td>\n",
              "      <td>0.311606</td>\n",
              "      <td>0.614646</td>\n",
              "      <td>0.060367</td>\n",
              "      <td>0.531559</td>\n",
              "      <td>0.190482</td>\n",
              "      <td>0.004040</td>\n",
              "      <td>0.011751</td>\n",
              "      <td>0.513266</td>\n",
              "      <td>0.440836</td>\n",
              "      <td>0.539564</td>\n",
              "      <td>0.174681</td>\n",
              "      <td>0.607398</td>\n",
              "      <td>0.449416</td>\n",
              "      <td>0.101524</td>\n",
              "      <td>0.159672</td>\n",
              "      <td>0.244949</td>\n",
              "      <td>0.153596</td>\n",
              "      <td>0.441872</td>\n",
              "      <td>0.119184</td>\n",
              "      <td>0.238309</td>\n",
              "      <td>0.096205</td>\n",
              "      <td>0.001507</td>\n",
              "      <td>0.026990</td>\n",
              "      <td>0.425961</td>\n",
              "      <td>0.281899</td>\n",
              "      <td>0.615545</td>\n",
              "      <td>0.133919</td>\n",
              "      <td>0.452272</td>\n",
              "      <td>0.191550</td>\n",
              "      <td>0.024786</td>\n",
              "      <td>0.053430</td>\n",
              "      <td>0.448168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.440542</td>\n",
              "      <td>0.441893</td>\n",
              "      <td>0.440137</td>\n",
              "      <td>0.440508</td>\n",
              "      <td>0.669824</td>\n",
              "      <td>0.440987</td>\n",
              "      <td>0.187976</td>\n",
              "      <td>0.398964</td>\n",
              "      <td>0.202366</td>\n",
              "      <td>0.434235</td>\n",
              "      <td>0.425404</td>\n",
              "      <td>0.267301</td>\n",
              "      <td>0.176228</td>\n",
              "      <td>0.318783</td>\n",
              "      <td>0.004867</td>\n",
              "      <td>0.024482</td>\n",
              "      <td>0.206077</td>\n",
              "      <td>0.475031</td>\n",
              "      <td>0.462841</td>\n",
              "      <td>0.424089</td>\n",
              "      <td>0.261478</td>\n",
              "      <td>0.368925</td>\n",
              "      <td>0.063191</td>\n",
              "      <td>0.131305</td>\n",
              "      <td>0.325204</td>\n",
              "      <td>0.083416</td>\n",
              "      <td>0.515858</td>\n",
              "      <td>0.168162</td>\n",
              "      <td>0.352007</td>\n",
              "      <td>0.054233</td>\n",
              "      <td>0.025444</td>\n",
              "      <td>0.143774</td>\n",
              "      <td>0.291209</td>\n",
              "      <td>0.168674</td>\n",
              "      <td>0.497531</td>\n",
              "      <td>0.027886</td>\n",
              "      <td>0.332575</td>\n",
              "      <td>0.072814</td>\n",
              "      <td>0.000722</td>\n",
              "      <td>0.021278</td>\n",
              "      <td>...</td>\n",
              "      <td>0.406368</td>\n",
              "      <td>0.482938</td>\n",
              "      <td>0.349272</td>\n",
              "      <td>0.389979</td>\n",
              "      <td>0.398535</td>\n",
              "      <td>0.051966</td>\n",
              "      <td>0.127961</td>\n",
              "      <td>0.478306</td>\n",
              "      <td>0.342071</td>\n",
              "      <td>0.583938</td>\n",
              "      <td>0.132277</td>\n",
              "      <td>0.532925</td>\n",
              "      <td>0.222117</td>\n",
              "      <td>0.001670</td>\n",
              "      <td>0.006387</td>\n",
              "      <td>0.504641</td>\n",
              "      <td>0.396672</td>\n",
              "      <td>0.482767</td>\n",
              "      <td>0.095158</td>\n",
              "      <td>0.572430</td>\n",
              "      <td>0.422047</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.249714</td>\n",
              "      <td>0.256804</td>\n",
              "      <td>0.441274</td>\n",
              "      <td>0.099619</td>\n",
              "      <td>0.243130</td>\n",
              "      <td>0.150464</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.420924</td>\n",
              "      <td>0.232492</td>\n",
              "      <td>0.589131</td>\n",
              "      <td>0.153001</td>\n",
              "      <td>0.443218</td>\n",
              "      <td>0.145499</td>\n",
              "      <td>0.070249</td>\n",
              "      <td>0.187655</td>\n",
              "      <td>0.443221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1793 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    var1(t-7)  var2(t-7)  var3(t-7)  ...  var255(t-1)  var256(t-1)   var6(t)\n",
              "8    0.430163   0.431369   0.429128  ...     0.037625     0.254125  0.443221\n",
              "9    0.435911   0.436790   0.430404  ...     0.000550     0.006923  0.443221\n",
              "10   0.442617   0.442212   0.435350  ...     0.036553     0.085632  0.443381\n",
              "11   0.440542   0.444126   0.440775  ...     0.024786     0.053430  0.448168\n",
              "12   0.440542   0.441893   0.440137  ...     0.070249     0.187655  0.443221\n",
              "\n",
              "[5 rows x 1793 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uw1xhWMYH69j",
        "colab_type": "text"
      },
      "source": [
        "## Split into train and test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YegC1VHvTcr5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_split = TRAIN_SPLIT #0.87"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1kkhGPsTFOe",
        "colab_type": "code",
        "outputId": "f1bbf319-7ef8-401c-ffa2-ba397d21d9d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "values = reframed.values\n",
        "num_data = len(values)\n",
        "num_data"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10991"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZPvLDA-H7VB",
        "colab_type": "code",
        "outputId": "74a45f04-1419-4bce-b10b-849b47479bed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "num_train = int(train_split * num_data)\n",
        "num_train"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9562"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUlhbObsTjO0",
        "colab_type": "code",
        "outputId": "545c73c0-f13a-4f1f-b3c4-fb47b8172b32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "num_test = num_data - num_train\n",
        "num_test"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1429"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPPvZt1ITVPD",
        "colab_type": "code",
        "outputId": "3012fd18-1000-4566-d908-ca90f0c76ce6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train = values[:num_train, :]\n",
        "test = values[num_train:, :]\n",
        "len(train) + len(test)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10991"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCmJvFINIB1W",
        "colab_type": "text"
      },
      "source": [
        "## Split into input and outputs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cnBlB2fICWY",
        "colab_type": "code",
        "outputId": "3a606184-0b35-4fb3-fe1c-b6db37eda099",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "n_obs = n_days * n_features\n",
        "train_X, train_y = train[:, :n_obs], train[:, -n_features]\n",
        "test_X, test_y = test[:, :n_obs], test[:, -n_features]\n",
        "svm_train_y = train_y.copy()\n",
        "svm_test_y = test_y.copy()\n",
        "\n",
        "print(train_X.shape, len(train_X), train_y.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9562, 1792) 9562 (9562,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkJk-L9EIHtB",
        "colab_type": "text"
      },
      "source": [
        "## Reshape input to be 3D [samples, timesteps, features]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4VB7gGhIIHn",
        "colab_type": "code",
        "outputId": "4d35eb08-e562-439f-ec3c-16bac04bf6d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_X = train_X.reshape((train_X.shape[0], n_days, n_features))\n",
        "test_X = test_X.reshape((test_X.shape[0], n_days, n_features))\n",
        "svm_train_X = train_X.copy()\n",
        "svm_test_X = test_X.copy()\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9562, 7, 256) (9562,) (1429, 7, 256) (1429,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5a9dpfMfIdgt",
        "colab_type": "text"
      },
      "source": [
        "## Design network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-m1vYwHjIeaa",
        "colab_type": "code",
        "outputId": "3b414878-25ce-4434-a6bd-12bc4b6b44eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(NEURONS, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True, name='lstm1'))\n",
        "model.add(LSTM(NEURONS, name='lstm2'))\n",
        "model.add(Dense(1, activation='linear', name='output'))\n",
        "optimizer = Adam(lr=LEARNING_RATE)\n",
        "model.compile(loss=LOSS_FUNCTION, optimizer=optimizer)\n",
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0624 02:32:13.785245 140686416684928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0624 02:32:13.802486 140686416684928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0624 02:32:13.805573 140686416684928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0624 02:32:14.416566 140686416684928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm1 (LSTM)                 (None, 7, 128)            197120    \n",
            "_________________________________________________________________\n",
            "lstm2 (LSTM)                 (None, 128)               131584    \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 328,833\n",
            "Trainable params: 328,833\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KLDLe5B9Pdl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toPbJTRQIkAq",
        "colab_type": "text"
      },
      "source": [
        "## Fit network\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3O5ZOucUIkZI",
        "colab_type": "code",
        "outputId": "0c4414b8-3632-46f4-bbfa-3655a0826fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3607
        }
      },
      "source": [
        "%%time\n",
        "history = model.fit(\n",
        "    train_X, \n",
        "    train_y, \n",
        "    epochs=EPOCHS, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    validation_data=(test_X, test_y), \n",
        "    verbose=1, \n",
        "    shuffle=False)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0624 02:32:14.789334 140686416684928 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0624 02:32:15.968796 140686416684928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "W0624 02:32:16.082584 140686416684928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 9562 samples, validate on 1429 samples\n",
            "Epoch 1/100\n",
            "9562/9562 [==============================] - 6s 631us/step - loss: 0.0213 - val_loss: 9.9533e-04\n",
            "Epoch 2/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 0.0213 - val_loss: 0.0066\n",
            "Epoch 3/100\n",
            "9562/9562 [==============================] - 4s 392us/step - loss: 0.0197 - val_loss: 0.0099\n",
            "Epoch 4/100\n",
            "9562/9562 [==============================] - 4s 395us/step - loss: 0.0196 - val_loss: 0.0131\n",
            "Epoch 5/100\n",
            "9562/9562 [==============================] - 4s 396us/step - loss: 0.0204 - val_loss: 0.0093\n",
            "Epoch 6/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 0.0194 - val_loss: 0.0104\n",
            "Epoch 7/100\n",
            "9562/9562 [==============================] - 4s 393us/step - loss: 0.0199 - val_loss: 0.0096\n",
            "Epoch 8/100\n",
            "9562/9562 [==============================] - 4s 394us/step - loss: 0.0173 - val_loss: 0.0034\n",
            "Epoch 9/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 0.0148 - val_loss: 0.0025\n",
            "Epoch 10/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 0.0134 - val_loss: 0.0031\n",
            "Epoch 11/100\n",
            "9562/9562 [==============================] - 4s 393us/step - loss: 0.0111 - val_loss: 0.0051\n",
            "Epoch 12/100\n",
            "9562/9562 [==============================] - 4s 388us/step - loss: 0.0122 - val_loss: 0.0030\n",
            "Epoch 13/100\n",
            "9562/9562 [==============================] - 4s 392us/step - loss: 0.0099 - val_loss: 0.0034\n",
            "Epoch 14/100\n",
            "9562/9562 [==============================] - 4s 392us/step - loss: 0.0099 - val_loss: 0.0037\n",
            "Epoch 15/100\n",
            "9562/9562 [==============================] - 4s 395us/step - loss: 0.0114 - val_loss: 0.0040\n",
            "Epoch 16/100\n",
            "9562/9562 [==============================] - 4s 397us/step - loss: 0.0105 - val_loss: 0.0057\n",
            "Epoch 17/100\n",
            "9562/9562 [==============================] - 4s 399us/step - loss: 0.0092 - val_loss: 0.0044\n",
            "Epoch 18/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 0.0082 - val_loss: 0.0050\n",
            "Epoch 19/100\n",
            "9562/9562 [==============================] - 4s 392us/step - loss: 0.0075 - val_loss: 0.0038\n",
            "Epoch 20/100\n",
            "9562/9562 [==============================] - 4s 396us/step - loss: 0.0069 - val_loss: 0.0034\n",
            "Epoch 21/100\n",
            "9562/9562 [==============================] - 4s 394us/step - loss: 0.0063 - val_loss: 0.0028\n",
            "Epoch 22/100\n",
            "9562/9562 [==============================] - 4s 397us/step - loss: 0.0060 - val_loss: 0.0029\n",
            "Epoch 23/100\n",
            "9562/9562 [==============================] - 4s 390us/step - loss: 0.0057 - val_loss: 0.0023\n",
            "Epoch 24/100\n",
            "9562/9562 [==============================] - 4s 383us/step - loss: 0.0054 - val_loss: 0.0011\n",
            "Epoch 25/100\n",
            "9562/9562 [==============================] - 4s 392us/step - loss: 0.0061 - val_loss: 0.0022\n",
            "Epoch 26/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 0.0048 - val_loss: 0.0018\n",
            "Epoch 27/100\n",
            "9562/9562 [==============================] - 4s 410us/step - loss: 0.0044 - val_loss: 0.0012\n",
            "Epoch 28/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 0.0047 - val_loss: 0.0013\n",
            "Epoch 29/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 0.0051 - val_loss: 0.0034\n",
            "Epoch 30/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 0.0043 - val_loss: 0.0019\n",
            "Epoch 31/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 0.0035 - val_loss: 5.8599e-04\n",
            "Epoch 32/100\n",
            "9562/9562 [==============================] - 4s 405us/step - loss: 0.0042 - val_loss: 0.0013\n",
            "Epoch 33/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 0.0038 - val_loss: 8.9491e-04\n",
            "Epoch 34/100\n",
            "9562/9562 [==============================] - 4s 396us/step - loss: 0.0043 - val_loss: 5.9474e-04\n",
            "Epoch 35/100\n",
            "9562/9562 [==============================] - 4s 395us/step - loss: 0.0043 - val_loss: 4.9862e-04\n",
            "Epoch 36/100\n",
            "9562/9562 [==============================] - 4s 395us/step - loss: 0.0039 - val_loss: 5.0783e-04\n",
            "Epoch 37/100\n",
            "9562/9562 [==============================] - 4s 397us/step - loss: 0.0038 - val_loss: 5.2910e-04\n",
            "Epoch 38/100\n",
            "9562/9562 [==============================] - 4s 404us/step - loss: 0.0037 - val_loss: 5.6502e-04\n",
            "Epoch 39/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 0.0034 - val_loss: 7.3905e-04\n",
            "Epoch 40/100\n",
            "9562/9562 [==============================] - 4s 411us/step - loss: 0.0032 - val_loss: 7.7835e-04\n",
            "Epoch 41/100\n",
            "9562/9562 [==============================] - 4s 410us/step - loss: 0.0028 - val_loss: 9.0257e-04\n",
            "Epoch 42/100\n",
            "9562/9562 [==============================] - 4s 417us/step - loss: 0.0026 - val_loss: 8.4058e-04\n",
            "Epoch 43/100\n",
            "9562/9562 [==============================] - 4s 405us/step - loss: 0.0024 - val_loss: 0.0010\n",
            "Epoch 44/100\n",
            "9562/9562 [==============================] - 4s 407us/step - loss: 0.0021 - val_loss: 7.0775e-04\n",
            "Epoch 45/100\n",
            "9562/9562 [==============================] - 4s 403us/step - loss: 0.0019 - val_loss: 8.6251e-04\n",
            "Epoch 46/100\n",
            "9562/9562 [==============================] - 4s 406us/step - loss: 0.0017 - val_loss: 7.0180e-04\n",
            "Epoch 47/100\n",
            "9562/9562 [==============================] - 4s 411us/step - loss: 0.0015 - val_loss: 6.3224e-04\n",
            "Epoch 48/100\n",
            "9562/9562 [==============================] - 4s 406us/step - loss: 0.0014 - val_loss: 5.1239e-04\n",
            "Epoch 49/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 0.0012 - val_loss: 4.7185e-04\n",
            "Epoch 50/100\n",
            "9562/9562 [==============================] - 4s 404us/step - loss: 0.0010 - val_loss: 4.4041e-04\n",
            "Epoch 51/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 9.5639e-04 - val_loss: 4.4190e-04\n",
            "Epoch 52/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 8.8292e-04 - val_loss: 3.8725e-04\n",
            "Epoch 53/100\n",
            "9562/9562 [==============================] - 4s 395us/step - loss: 8.4613e-04 - val_loss: 3.3543e-04\n",
            "Epoch 54/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 8.1308e-04 - val_loss: 2.6799e-04\n",
            "Epoch 55/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 8.0837e-04 - val_loss: 2.3125e-04\n",
            "Epoch 56/100\n",
            "9562/9562 [==============================] - 4s 408us/step - loss: 8.3229e-04 - val_loss: 2.1287e-04\n",
            "Epoch 57/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 8.6378e-04 - val_loss: 2.0749e-04\n",
            "Epoch 58/100\n",
            "9562/9562 [==============================] - 4s 409us/step - loss: 8.7830e-04 - val_loss: 2.1576e-04\n",
            "Epoch 59/100\n",
            "9562/9562 [==============================] - 4s 396us/step - loss: 8.6788e-04 - val_loss: 2.1892e-04\n",
            "Epoch 60/100\n",
            "9562/9562 [==============================] - 4s 391us/step - loss: 8.4096e-04 - val_loss: 2.2225e-04\n",
            "Epoch 61/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 8.3560e-04 - val_loss: 2.2628e-04\n",
            "Epoch 62/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 8.7047e-04 - val_loss: 2.3478e-04\n",
            "Epoch 63/100\n",
            "9562/9562 [==============================] - 4s 399us/step - loss: 9.0441e-04 - val_loss: 2.4169e-04\n",
            "Epoch 64/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 9.8787e-04 - val_loss: 2.5816e-04\n",
            "Epoch 65/100\n",
            "9562/9562 [==============================] - 4s 405us/step - loss: 0.0010 - val_loss: 2.7168e-04\n",
            "Epoch 66/100\n",
            "9562/9562 [==============================] - 4s 404us/step - loss: 0.0011 - val_loss: 2.9947e-04\n",
            "Epoch 67/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 0.0013 - val_loss: 3.1967e-04\n",
            "Epoch 68/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 0.0015 - val_loss: 2.9842e-04\n",
            "Epoch 69/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 0.0016 - val_loss: 2.5487e-04\n",
            "Epoch 70/100\n",
            "9562/9562 [==============================] - 4s 403us/step - loss: 0.0018 - val_loss: 2.3770e-04\n",
            "Epoch 71/100\n",
            "9562/9562 [==============================] - 4s 406us/step - loss: 0.0019 - val_loss: 2.4791e-04\n",
            "Epoch 72/100\n",
            "9562/9562 [==============================] - 4s 406us/step - loss: 0.0017 - val_loss: 2.1846e-04\n",
            "Epoch 73/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 0.0016 - val_loss: 2.0754e-04\n",
            "Epoch 74/100\n",
            "9562/9562 [==============================] - 4s 403us/step - loss: 0.0014 - val_loss: 2.0628e-04\n",
            "Epoch 75/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 0.0011 - val_loss: 1.9999e-04\n",
            "Epoch 76/100\n",
            "9562/9562 [==============================] - 4s 397us/step - loss: 0.0010 - val_loss: 1.3090e-04\n",
            "Epoch 77/100\n",
            "9562/9562 [==============================] - 4s 407us/step - loss: 9.2863e-04 - val_loss: 1.4561e-04\n",
            "Epoch 78/100\n",
            "9562/9562 [==============================] - 4s 406us/step - loss: 9.3279e-04 - val_loss: 1.3539e-04\n",
            "Epoch 79/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 8.3123e-04 - val_loss: 1.4029e-04\n",
            "Epoch 80/100\n",
            "9562/9562 [==============================] - 4s 403us/step - loss: 8.4409e-04 - val_loss: 1.2064e-04\n",
            "Epoch 81/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 8.1963e-04 - val_loss: 1.0849e-04\n",
            "Epoch 82/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 8.5764e-04 - val_loss: 1.1075e-04\n",
            "Epoch 83/100\n",
            "9562/9562 [==============================] - 4s 407us/step - loss: 8.4679e-04 - val_loss: 1.0781e-04\n",
            "Epoch 84/100\n",
            "9562/9562 [==============================] - 4s 404us/step - loss: 8.5282e-04 - val_loss: 1.0818e-04\n",
            "Epoch 85/100\n",
            "9562/9562 [==============================] - 4s 404us/step - loss: 8.2400e-04 - val_loss: 1.1830e-04\n",
            "Epoch 86/100\n",
            "9562/9562 [==============================] - 4s 401us/step - loss: 7.8238e-04 - val_loss: 8.7973e-05\n",
            "Epoch 87/100\n",
            "9562/9562 [==============================] - 4s 402us/step - loss: 7.3062e-04 - val_loss: 8.1412e-05\n",
            "Epoch 88/100\n",
            "9562/9562 [==============================] - 4s 399us/step - loss: 7.1251e-04 - val_loss: 7.7973e-05\n",
            "Epoch 89/100\n",
            "9562/9562 [==============================] - 4s 403us/step - loss: 6.2634e-04 - val_loss: 7.2460e-05\n",
            "Epoch 90/100\n",
            "9562/9562 [==============================] - 4s 403us/step - loss: 7.0703e-04 - val_loss: 2.3997e-04\n",
            "Epoch 91/100\n",
            "9562/9562 [==============================] - 4s 405us/step - loss: 6.3491e-04 - val_loss: 6.5587e-04\n",
            "Epoch 92/100\n",
            "9562/9562 [==============================] - 4s 400us/step - loss: 5.6135e-04 - val_loss: 2.4525e-04\n",
            "Epoch 93/100\n",
            "9562/9562 [==============================] - 4s 406us/step - loss: 6.4812e-04 - val_loss: 2.5335e-04\n",
            "Epoch 94/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 7.6838e-04 - val_loss: 1.4229e-04\n",
            "Epoch 95/100\n",
            "9562/9562 [==============================] - 4s 398us/step - loss: 6.5628e-04 - val_loss: 4.3427e-04\n",
            "Epoch 96/100\n",
            "9562/9562 [==============================] - 4s 399us/step - loss: 6.5034e-04 - val_loss: 2.1141e-04\n",
            "Epoch 97/100\n",
            "9562/9562 [==============================] - 4s 394us/step - loss: 6.1157e-04 - val_loss: 1.5267e-04\n",
            "Epoch 98/100\n",
            "9562/9562 [==============================] - 4s 392us/step - loss: 6.3231e-04 - val_loss: 1.5220e-04\n",
            "Epoch 99/100\n",
            "9562/9562 [==============================] - 4s 396us/step - loss: 6.2547e-04 - val_loss: 2.0398e-04\n",
            "Epoch 100/100\n",
            "9562/9562 [==============================] - 4s 385us/step - loss: 6.1580e-04 - val_loss: 1.4399e-04\n",
            "CPU times: user 8min 15s, sys: 29.4 s, total: 8min 45s\n",
            "Wall time: 6min 26s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BFBxpWTIzfo",
        "colab_type": "text"
      },
      "source": [
        "## Plot history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olVM4PlfI0aT",
        "colab_type": "code",
        "outputId": "521ae79d-ef6f-4f4f-b645-bfacffe0dda2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.figure(figsize=default)\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W8YKjqQac8WCzd7/v8UT2oMnI02KukO6pkk2QNBomXQYH1x+t3fSztLhIxg+L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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUlsH46MI45n",
        "colab_type": "text"
      },
      "source": [
        "## Make a prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsMo4oRZI5ch",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yhat = model.predict(test_X)\n",
        "test_X = test_X.reshape((test_X.shape[0], n_days*n_features))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPCc-7kkI_Hp",
        "colab_type": "text"
      },
      "source": [
        "## Invert scaling for forecast"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQZz0lCtI_ij",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inv_yhat = concatenate((yhat, test_X[:, -(n_features-1):]), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RhnbjBhfJE_C",
        "colab_type": "text"
      },
      "source": [
        "## Invert scaling for actual"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KCHHP2JlJF78",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_y = test_y.reshape((len(test_y), 1))\n",
        "inv_y = concatenate((test_y, test_X[:, -(n_features-1):]), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U_dE3WMJK9z",
        "colab_type": "text"
      },
      "source": [
        "## Calculate RMSE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Sm3rLU3JLf1",
        "colab_type": "code",
        "outputId": "11edfe1a-6c8b-4cf8-fc6f-a128b88a29c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "rmse = sqrt(mean_squared_error(inv_y, inv_yhat))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 18.789\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ow7xwT45imqk",
        "colab_type": "text"
      },
      "source": [
        "## Plot prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2k_F5OWwiokB",
        "colab_type": "code",
        "outputId": "c085cc40-ecd3-4069-d7ab-5e7eb8b3591f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "  # Get the output-signal predicted by the model.\n",
        "  signal_pred = inv_yhat\n",
        "\n",
        "  # Get the true output-signal from the data-set.\n",
        "  signal_true = inv_y\n",
        "\n",
        "  # Make the plotting-canvas bigger.\n",
        "  plt.figure(figsize=(13,5))\n",
        "\n",
        "  # Plot and compare the two signals.\n",
        "  plt.plot(signal_true, label='true')\n",
        "  plt.plot(signal_pred, label='pred')\n",
        "\n",
        "  # Plot labels etc.\n",
        "  plt.ylabel(TARGET_NAME)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lliilljQ0NAzm6YtRRGtdNPMwrsL8iv74oZXEkik2Nnby8PLtAIwtDwBw2LTqvOdVWGs+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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5e2pZBh9ZZ3i",
        "colab_type": "text"
      },
      "source": [
        "# Compare results with a SVM model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CkkwMyrtaJa5",
        "colab_type": "text"
      },
      "source": [
        "## Creating a SVM  for regression (SVR)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiBWrVXLZfBz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = svm.SVR() #svm.SVR(kernel='rbf') #svm.SVR()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzD2TddvaQQx",
        "colab_type": "text"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Do0TbZt81otW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "svm_train_X_1 = svm_train_X.reshape((svm_train_X.shape[0], n_days*n_features))\n",
        "svm_test_X_1 = svm_test_X.reshape((svm_test_X.shape[0], n_days*n_features))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3ZlT9bwaP54",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "97e4b6cc-a304-41be-e9f1-aa72a3f57952"
      },
      "source": [
        "clf.fit(svm_train_X_1, svm_train_y) #train"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
              "    gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KKzxKU0puDN8"
      },
      "source": [
        "## Make a prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lEQ653sXuCyv",
        "colab": {}
      },
      "source": [
        "svm_yhat = clf.predict(svm_test_X_1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "j3bKjYUJuOMM"
      },
      "source": [
        "## Invert scaling for forecast (return values to original range)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oM9IPw_0uOMP",
        "colab": {}
      },
      "source": [
        "svm_inv_yhat = concatenate((svm_yhat.reshape(-1,1), svm_test_X_1[:,-(n_features-1):]), axis=1)\n",
        "svm_inv_yhat = scaler.inverse_transform(svm_inv_yhat)\n",
        "svm_inv_yhat = svm_inv_yhat[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7ZcNjLb1uOMT"
      },
      "source": [
        "## Invert scaling for actual (return values to original range)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wvkY9IUouOMU",
        "colab": {}
      },
      "source": [
        "svm_test_y = svm_test_y.reshape((len(svm_test_y), 1))\n",
        "svm_inv_y = concatenate((svm_test_y, svm_test_X_1[:, -(n_features-1):]), axis=1)\n",
        "svm_inv_y = scaler.inverse_transform(svm_inv_y)\n",
        "svm_inv_y = svm_inv_y[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "o7DG-s1duCZQ"
      },
      "source": [
        "## Calculate RMSE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "d846e20e-bf8e-46c3-a569-3cd564d49d46",
        "id": "uR7iqMXZuCYc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "svm_rmse = sqrt(mean_squared_error(svm_inv_y, svm_inv_yhat))\n",
        "print('Test RMSE: %.3f' % svm_rmse)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test RMSE: 178.381\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4e2OHuD2BoHy"
      },
      "source": [
        "## Plot prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "986f2e23-11f5-4f72-b93c-a3884b0ccb70",
        "id": "yVBu24YOBoH1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "    # Make the plotting-canvas bigger.\n",
        "  plt.figure(figsize=(13,5))\n",
        "\n",
        "  # Plot and compare the two signals.\n",
        "  plt.plot(svm_inv_y, label='true')\n",
        "  plt.plot(inv_yhat, label='LSTM')\n",
        "  plt.plot(svm_inv_yhat, label='SVM-SVR')\n",
        "\n",
        "  # Plot labels etc.\n",
        "  plt.ylabel(TARGET_NAME)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q0dVAoe/Vfq/S37c/L/Z+EQupccsvUfMgaImIyINAgF5GtUquumIo2U5rx9QuU/nm6Dfq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i4PLu9WsgzAUGzZfxiDjRYs2l2Cn80nYapxDdbtOYpRbENXGfyeakTME1cXKRuA+aqu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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvxN-e1Tyv6M",
        "colab_type": "text"
      },
      "source": [
        "# Extra"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6-pSswZyJnk",
        "colab_type": "text"
      },
      "source": [
        "## Save model to JSON"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1M49FU8MyMEw",
        "colab_type": "code",
        "outputId": "8bf402da-e7b0-4410-db9f-d069a7ed5896",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "if not os.path.isdir(MODEL_PATH):\n",
        "    os.makedirs(MODEL_PATH)\n",
        "with open(MODEL_PATH+\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights(MODEL_PATH+\"model.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O0M0CNvbyLe5",
        "colab_type": "text"
      },
      "source": [
        "## Load json and create model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vG-EJNNysJ8",
        "colab_type": "code",
        "outputId": "9d2d49a3-8258-443c-e7a1-686590fd81fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# load json and create model\n",
        "json_file = open(MODEL_PATH+'model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights(MODEL_PATH+\"model.h5\")\n",
        "print(\"Loaded model from disk\")\n",
        "loaded_model.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm1 (LSTM)                 (None, 7, 128)            197120    \n",
            "_________________________________________________________________\n",
            "lstm2 (LSTM)                 (None, 128)               131584    \n",
            "_________________________________________________________________\n",
            "output (Dense)               (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 328,833\n",
            "Trainable params: 328,833\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e4STNFJvTIfB",
        "colab_type": "text"
      },
      "source": [
        "# License (MIT)\n",
        "\n",
        "Copyright (c) 2018 by [Marlon Franco]()\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
      ]
    }
  ]
}