9_Loss Functions in Forecasting.ipynb

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      "source": [
        "HSE, Applied Time Series Forecasitng , Winter 2023-24\n",
        "\n",
        "<font color=\"green\"> Lesson #9: Measuring Forecast Accuracy. Loss Functions in Foreasting</font>\n",
        "\n",
        "<span style=\"color:black; font-size: 12pt\"></span>\n",
        "\n",
        "Alexey Romanenko,\n",
        "<font color=\"blue\">alexromsput@gmail.com</font>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylqVvPXCKAE0"
      },
      "source": [
        "**Key words:**\n",
        "    - Loss functions: MAPE, MAPPE, WAPE, WAPPE, WAMAXPE, Hold-out period\n",
        "    \n",
        "**Your feedback:**  please provide you feedback  <a href=\"https://forms.gle/bEV5iwmTqdM9rUoy8\"> here </a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "09tdI0LEKAE1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from datetime import timedelta\n",
        "from scipy import stats\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.tsa import stattools\n",
        "from copy import deepcopy\n",
        "\n",
        "# don't forget to upload utils file to the colab session\n",
        "# from utils import qualityMACAPE, qualityRMSE\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots\n",
        "pd.options.plotting.backend = \"plotly\"\n",
        "\n",
        "# setting of the plotting style, registers pandas date converters for matplotlib and the default figure size\n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "pd.plotting.register_matplotlib_converters()\n",
        "# Default figure size\n",
        "sns.mpl.rc(\"figure\", figsize=(25, 5))\n",
        "sns.mpl.rc(\"font\", size=14)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k48N0_neLK1M"
      },
      "source": [
        "# Loss Functions for Time Series Forecasting\n",
        "**Questions**\n",
        "  * What loss functions for regression tasks do you know?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XG8GVl8kJdE5"
      },
      "source": [
        "$$  SSE =\\sum_{t=1}^T  \\left({A_t-F_t}\\right)^2 $$\n",
        "$$  MSE =\\frac{1}{T}\\sum_{t=1}^T  \\left({A_t-F_t}\\right)^2 $$\n",
        "$$  RMSE =\\sqrt{\\frac{1}{T}\\sum_{t=1}^T  \\left({A_t-F_t}\\right)^2} $$\n",
        "$$  MAE =\\frac{1}{T}\\sum_{t=1}^T  \\left|{A_t-F_t}\\right| $$\n",
        "$$  MAPE =\\frac{1}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t}\\right| $$\n",
        "$$  MAPPE =\\frac{1}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{F_t}\\right| $$\n",
        "$$  SMAPE =\\frac{2}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t+F_t}\\right| $$\n",
        "$$  MAMAXPE =\\frac{1}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{\\max(A_t,F_t)}\\right| $$\n",
        "$$  MASE = \\frac{2}{T-R+1} \\sum_{t=R}^T \\left|\\frac{A_t-F_t}{A_t-A_{t-1}}\\right| $$\n",
        "<br></br>\n",
        "$$  MedianAE = Median \\bigl(\\left|A_1-F_1\\right|,\\dots,\\left|A_T-F_T\\right|\\bigr) $$\n",
        "<br></br>\n",
        "____________________________________________________________________________\n",
        "**Closer to Business Metrics**\n",
        "* from MAPE to WAPE:\n",
        "   $$  MAPE =\\frac{1}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t}\\right| =\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t}\\right|*\\underbrace{\\frac{1}{T}}_{\\color{red}w} $$\n",
        "where $w$ is a weight of observation in the moment $t$\n",
        "* Let's assume that $w = \\frac{|A_t|}{\\sum_{t=1}^T |A_t|}$\n",
        "\n",
        "$$  WAPE =\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t}\\right|*w=\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t}\\right|*\\frac{|A_t|}{\\sum_{t=1}^T |A_t|} = \\sum_{t=1}^T  \\frac{\\left|A_t-F_t \\right|}{\\color{red}{|A_t|}}*\\frac{\\color{red}{|A_t|}}{\\sum_{t=1}^T |A_t|} = \\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\left|A_t\\right|}  $$\n",
        "\n",
        "<br></br>\n",
        "$$  WAPE = \\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\left|A_t\\right|} $$\n",
        "\n",
        "\n",
        "$$  WAPPE =\\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\left|F_t\\right|} $$\n",
        "$$  WAMAXPE =\\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\max(\\left|A_t\\right|,\\left|F_t\\right| )} $$\n",
        "\n",
        "* Note: usually, $sum()$ index includes time dimension, as well as organizational dimensions (product, locations, customers, clients, channels, etc.).  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "92eFAvA1JeTa"
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      "outputs": [],
      "source": [
        "# Quality functions\n",
        "def qualitySSE(x,y):\n",
        "    # Sum squared error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    return ((x-y)**2).sum(), (x-y)**2\n",
        "\n",
        "def qualityMSE(x,y):\n",
        "    # Mean squared error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    return ((x-y)**2).mean() , (x-y)**2\n",
        "\n",
        "def qualityRMSE(x,y):\n",
        "    # Root mean squared error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    return (((x-y)**2).mean())**(0.5) , (x-y).abs()\n",
        "\n",
        "def qualityMAE(x,y):\n",
        "    # Mean absolute error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    return (x-y).abs().mean(), (x-y).abs()\n",
        "\n",
        "def qualityMAPE(x,y):\n",
        "    # Mean absolute percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    qlt = ((x-y).abs()/x.abs()).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.mean() , qlt\n",
        "\n",
        "def qualityMAPPE(x,y):\n",
        "    # Mean absolute predicted percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    qlt = ((x-y).abs()/y.abs()).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.mean() , qlt\n",
        "\n",
        "def qualitySMAPE(x,y):\n",
        "    # Symmetric mean absolute percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    qlt = (2*(x-y).abs()/(x+y)).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.mean() , qlt\n",
        "\n",
        "def qualityMAMAXPE(x,y):\n",
        "    # Mean absolute maximum percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    qlt = ((x-y).abs()/max(x.abs(), y.max())).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.mean() , qlt\n",
        "\n",
        "def qualityMASE(x,y, init_step=0):\n",
        "    # Mean absolute scaled error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    qlt = ((x-y).abs()/(x-x.shift(1)).abs())[init_step:].replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.mean() , qlt\n",
        "\n",
        "def qualityMedianAE(x,y):\n",
        "    # Median absolute error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    return ((x-y).abs()).median(), (x-y).abs()\n",
        "\n",
        "def qualityWAPE(x,y):\n",
        "    # Weighted absolute percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    denom = x.abs().sum()\n",
        "    qlt = ((x-y).abs()/denom).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.sum() , qlt\n",
        "\n",
        "def qualityWAPPE(x,y):\n",
        "    # Weighted absolute percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    denom = y.abs().sum()\n",
        "    qlt = ((x-y).abs()/denom).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.sum() , qlt\n",
        "\n",
        "def qualityWAMAXPE(x,y):\n",
        "    # Weighted absolute maximum percentage error\n",
        "    # x,y - pandas structures\n",
        "    # x - real values\n",
        "    # y - forecasts\n",
        "    denom = pd.merge(x, y, right_index = True, left_index = True).max(axis = 1).sum()\n",
        "    qlt = ((x-y).abs()/denom).replace([np.inf, -np.inf], np.nan)\n",
        "    return qlt.sum() , qlt"
      ]
    },
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          },
          "metadata": {}
        }
      ],
      "source": [
        "length = 500\n",
        "frc_ts = pd.DataFrame([[1+x, length-(1+x)**(0.9)-100] for x in range(length)], columns = ['x','y'])frc_ts.plot().update_layout(height=350, width=1300)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fig = plt.figure(figsize = (25,35))\n",
        "# ax1=fig.add_subplot(111)\n",
        "qlt_array=['SSE', 'MSE', 'RMSE', 'MAE', 'MAPE', 'MAPPE', 'SMAPE', 'MASE', 'MedianAE', 'WAPE', 'WAPPE', 'WAMAXPE']\n",
        "qlt = [np.NaN]*len(qlt_array)\n",
        "fig = make_subplots(rows=4, cols=3)\n",
        "\n",
        "qlt_desc = [r\"$SSE =\\sum_{t=1}^T  \\left({A_t-F_t}\\right)^2 $\",\n",
        "r\"$MSE =\\frac{1}{T}\\sum_{t=1}^T  \\left({A_t-F_t}\\right)^2 $\",\n",
        "r\"$RMSE =\\sqrt{\\frac{1}{T}\\sum_{t=1}^T  \\left({A_t-F_t}\\right)^2} $\",\n",
        "r\"$MAE =\\frac{1}{T}\\sum_{t=1}^T  \\left|{A_t-F_t}\\right| $\",\n",
        "r\"$MAPE =\\frac{1}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t}\\right| $\",\n",
        "r\"$MAPPE =\\frac{1}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{F_t}\\right| $\",\n",
        "r\"$SMAPE =\\frac{2}{T}\\sum_{t=1}^T  \\left|\\frac{A_t-F_t}{A_t+F_t}\\right| $\",\n",
        "r\"$MASE = \\frac{2}{T-R+1} \\sum_{t=R}^T \\left|\\frac{A_t-F_t}{A_t-A_{t-1}}\\right| $\",\n",
        "r\"$MedianAE = Median \\left(\\left|A_1-F_1\\right|,\\dots,\\left|A_T-F_T\\right|\\right) $\",\n",
        "r\"$WAPE =\\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\left|A_t\\right|} $\",\n",
        "r\"$WAPPE =\\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\left|F_t\\right|} $\",\n",
        "r\"$WAMAXPE =\\frac{\\sum_{t=1}^T\\left|A_t-F_t\\right|}{\\sum_{t=1}^T\\max(\\left|A_t\\right|,\\left|F_t\\right| )} $\"]\n",
        "\n",
        "\n",
        "for i in range(len(qlt_array)):\n",
        "  qlt[i], qlt_vec = eval('quality'+qlt_array[i])(frc_ts['x'], frc_ts['y'])\n",
        "  qlt_vec.index = frc_ts['x']-frc_ts['y']\n",
        "  fig.add_trace(\n",
        "      go.Scatter(x=(frc_ts['x']-frc_ts['y']), y=qlt_vec,\n",
        "      name=qlt_desc[i]),\n",
        "      row=i//3+1, col=i%3+1\n",
        "  )\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "FmXBeXmYV31l",
        "outputId": "49c3ed07-dc3f-462b-da95-5a6b3c1250e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "**Let's consider random signal $y_t: {\\bf{E}}(y_t, y_{t+\\tau}) = 0, \\forall \\tau>0$**.\n",
        "\n",
        "Optimal forecast:\n",
        "$$\\hat y^* = \\arg\\min_{\\hat y}\\left(\\int_{-\\infty}^{+\\infty}Loss(y, \\hat y)\\cdot~p(y) dy\\right)$$\n",
        "\n",
        "**Questions**:\n",
        "  * What is the optimal forecast for prediction of such $y_t$?\n",
        "  * What is the optimal forecast for prediction of such $y_t$ w.r.t. SSE (RMSE) loss function?\n",
        "  \n",
        "  $\\hat y^* = {\\mathbf E}y$\n",
        "\n",
        "  * What is the optimal forecast for prediction of such $y_t$ w.r.t. MAE loss function?\n",
        "\n",
        "  $\\hat y^* = F^{-1}(0.5)~-$ median\n",
        "\n",
        "  * What is the optimal forecast for prediction of such $y_t$ w.r.t. MAPE/MAPPE/WAPE/WAPPE/WAMAXPE loss functions?\n",
        "  "
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        "outputId": "1a88bc16-476b-44e0-9710-490a16279985"
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          "metadata": {}
        }
      ],
      "source": [
        "# generate random variable\n",
        "shape, scale = 2., 2.  # mean=2, std=2*sqrt(2)\n",
        "y = np.random.gamma(shape, scale, 1000)\n",
        "pd.DataFrame(y).plot().update_layout(height=350, width=1300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iNRiqD43K1j",
        "outputId": "4c8c3852-5d5a-4415-ac3b-de397559579a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.32811656]\n"
          ]
        }
      ],
      "source": [
        "# self-practice task: realize search for optimal value wrt to all loss functions above\n",
        "from scipy.optimize import minimize\n",
        "# def rosen(x):\n",
        "#     \"\"\"The Rosenbrock function\"\"\"\n",
        "#     return sum(100.0*(x[1:]-x[:-1]**2.0)**2.0 + (1-x[:-1])**2.0)\n",
        "\n",
        "def quality_real_set(x=0, qlt='WAPE'):\n",
        "  return  eval('quality'+qlt)(pd.DataFrame(y, columns=['Y'])['Y'], pd.DataFrame([x]*len(y), columns=['X'])['X'])[0]\n",
        "\n",
        "x0 = 0.1\n",
        "res = minimize(quality_real_set, x0, 'MAE')\n",
        "print(res.x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "WFoAL5VBt_5w",
        "outputId": "70c0f284-34e7-41d6-8c8d-aeb37b544b3c"
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      "outputs": [
        {
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          "metadata": {}
        }
      ],
      "source": [
        "# plot histogramm\n",
        "fig = px.histogram(pd.DataFrame(y, columns = ['ts values']), x='ts values')\n",
        "for qlt_func in ['WAPPE', 'WAPE', 'RMSE', 'MAE', 'WAMAXPE']:\n",
        "  fig.add_vline(x=minimize(quality_real_set, x0, qlt_func).x[0], line_dash = 'dash', line_color = 'firebrick', annotation_text=qlt_func)\n",
        "# fig.add_vline(x=np.mean(y), line_dash = 'dash', line_color = 'blue', annotation_text=qlt_func)\n",
        "\n",
        "fig.update_layout(height=350, width=1300).show()"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqAIXdY49MQK"
      },
      "source": [
        "<img src = 'https://github.com/aromanenko/ATSF/blob/main/pic/Quality%20Comparison.png?raw=true'>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SYIWvpmEKAFW"
      },
      "source": [
        "# Selecting the Best Time Series Forecasting Algorithm\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzcSCLwclHJo"
      },
      "source": [
        "**Two-steps CV approach**\n",
        "<img src = \"https://miro.medium.com/max/1400/1*OsY2PjdFJOIsQj9E5S34cQ.png\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "0mHZxfjtcX7q",
        "outputId": "db209965-af06-4b07-a518-866ac062a54d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "# Wage\n",
        "ts_wage = pd.read_csv('https://raw.githubusercontent.com/aromanenko/ATSF/33b78c9be63831567f8036ad40c5859e824d30f7/data/monthly-wage.csv', sep=';', decimal='.', parse_dates=True, index_col='Month')\n",
        "ts_wage.index.names=['Timestamp']\n",
        "ts_wage = ts_wage['01-01-1999':]\n",
        "ts_wage.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "IOfTy8_WcnWy",
        "outputId": "1a69824a-9d81-42dc-9279-241d89725213"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpb4f1f6ke/l0mfczrc.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpb4f1f6ke/ot7rrpa7.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=84512', 'data', 'file=/tmp/tmpb4f1f6ke/l0mfczrc.json', 'init=/tmp/tmpb4f1f6ke/ot7rrpa7.json', 'output', 'file=/tmp/tmpb4f1f6ke/prophet_modelsytc6jt_/prophet_model-20240125203303.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "20:33:03 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "20:33:03 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a6c165e5a80>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# forecasting with prophet\n",
        "# !pip install prophet\n",
        "\n",
        "from prophet import Prophet\n",
        "#suppressing the info logs\n",
        "import logging\n",
        "logging.getLogger('fbprophet').setLevel(logging.ERROR)\n",
        "\n",
        "#creating a dataframe that fbprophet requires\n",
        "# df = pd.DataFrame([col])\n",
        "# df['ds'] = df.index\n",
        "# df.columns = ['y', 'ds']\n",
        "\n",
        "#making predictions\n",
        "m = Prophet()\n",
        "m.fit(ts_wage[:test_period_start].reset_index().rename(columns={'Real wage':'y', 'Timestamp':'ds'}))\n",
        "#'make_future_dataframe' parameter decides how far into the future we're looking\n",
        "future = m.make_future_dataframe(12,freq = 'M')\n",
        "forecast = m.predict(future)\n",
        "\n",
        "test_loss = eval('quality' + loss)(ts_wage.loc[test_period_start:test_period_end, 'Real wage'], forecast.set_index('ds').loc[test_period_start:test_period_end, 'yhat'])[0]\n",
        "\n",
        "#plotting the results\n",
        "plt.figure()\n",
        "plt.plot(ts_wage.loc[:test_period_start].index, ts_wage.loc[:test_period_start], label='Train')\n",
        "plt.plot(ts_wage.loc[test_period_start:].index, ts_wage.loc[test_period_start:, 'Real wage'], label='Test')\n",
        "plt.plot(forecast.set_index('ds').loc[test_period_start:].index, forecast.set_index('ds').loc[test_period_start:,'yhat'], color='green', alpha=0.7, label='fbprophet')\n",
        "plt.title('wage forecast by fbprphet, test period {0} = {1}'.format(loss, eval('quality' + loss)(ts_wage.loc[test_period_start:test_period_end, 'Real wage'], forecast.set_index('ds').loc[test_period_start:test_period_end, 'yhat'])[0]))\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7h8AgPFedFi2"
      },
      "outputs": [],
      "source": [
        "test_period_start = '2016-01-01'\n",
        "test_period_end = '2018-01-01'\n",
        "loss = 'SMAPE'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "73hunoW2cBkh",
        "outputId": "300199d4-2c25-4046-9301-b64acc1846cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a6c148de440>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# statmodels TW\n",
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "\n",
        "train, test = ts_wage.loc[:test_period_start, 'Real wage'], ts_wage.loc[test_period_start:, 'Real wage']\n",
        "model = ExponentialSmoothing(train, trend = 'add', seasonal='add', seasonal_periods=12).fit()\n",
        "twpred = model.predict(start=test.index[0], end=test.index[-1])\n",
        "\n",
        "plt.plot(train.index, train, label='Train')\n",
        "plt.plot(test.index, test, label='Test')\n",
        "plt.plot(twpred.index, twpred, label='Theil-Wage')\n",
        "plt.title('wage forecast by smThailWage, test period {0} = {1}'.format(loss, eval('quality' + loss)(test, twpred)[0]))\n",
        "plt.legend(loc='best')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQ7LGMsIlqFO",
        "outputId": "1a04f73f-a05c-4c4b-ceda-04bc6a062f07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sarimax_010_010: aic=1076.110259, bic=1079.367754, hqic=1077.429569 \n",
            "sarimax_011_010: aic=1071.458680, bic=1077.973671, hqic=1074.097299 \n",
            "sarimax_110_010: aic=1071.034689, bic=1077.549679, hqic=1073.673308 \n",
            "sarimax_111_010: aic=1073.019611, bic=1082.792097, hqic=1076.977540 \n",
            "sarimax_010_011: aic=1077.986771, bic=1084.501761, hqic=1080.625390 \n",
            "sarimax_011_011: aic=1073.362833, bic=1083.135319, hqic=1077.320762 \n",
            "sarimax_110_011: aic=1072.960699, bic=1082.733186, hqic=1076.918628 \n",
            "sarimax_111_011: aic=1074.955882, bic=1087.985863, hqic=1080.233120 \n",
            "sarimax_010_110: aic=1077.949908, bic=1084.464898, hqic=1080.588527 \n",
            "sarimax_011_110: aic=1073.331118, bic=1083.103604, hqic=1077.289047 \n",
            "sarimax_110_110: aic=1072.937026, bic=1082.709512, hqic=1076.894955 \n",
            "sarimax_111_110: aic=1074.934342, bic=1087.964323, hqic=1080.211580 \n",
            "sarimax_010_111: aic=1074.926260, bic=1084.698746, hqic=1078.884189 \n",
            "sarimax_011_111: aic=1071.901608, bic=1084.931589, hqic=1077.178846 \n",
            "sarimax_110_111: aic=1071.424301, bic=1084.454282, hqic=1076.701539 \n",
            "sarimax_111_111: aic=1073.311313, bic=1089.598790, hqic=1079.907861 \n"
          ]
        }
      ],
      "source": [
        "# statmodels SARIMAX\n",
        "def fit_arima_12(ts, p,d,q,P,D,Q, exogs_var = None):\n",
        "    try:\n",
        "        mod = sm.tsa.statespace.SARIMAX(endog = ts, order=(p,d,q), seasonal_order=(P,D,Q,12), exog = exogs_var)\n",
        "        results = mod.fit()\n",
        "        return(\"sarimax_%i%i%i_%i%i%i: aic=%f, bic=%f, hqic=%f \" % (p,d,q,P,D,Q,\n",
        "               results.aic, results.bic, results.hqic))\n",
        "    except Exception:\n",
        "        return (\"sarimax_%i%i%i_%i%i%i: can't fit model \" % (p,d,q,P,D,Q))\n",
        "\n",
        "\n",
        "# Qaution: we assume that the appropriate d = 1, D = 1\n",
        "# try most popular parameters of ARIMA\n",
        "from sklearn.model_selection import ParameterGrid\n",
        "\n",
        "param_grid = {'p': [0, 1], 'd' : [1],\n",
        "             'q': [0, 1], 'P' : [0, 1],\n",
        "             'D': [1], 'Q' : [0,1]}\n",
        "\n",
        "grid = ParameterGrid(param_grid)\n",
        "\n",
        "for params in grid:\n",
        "    print(fit_arima_12(ts_wage.loc[:test_period_start, 'Real wage'],params['p'], params['d'], params['q'], params['P'], params['D'], params['Q']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "vr4CUfnynHaV",
        "outputId": "1b6e6f96-abc1-44ae-9ca1-32cc8811a667"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7a6c10685900>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "sarimax_110_010 = sm.tsa.statespace.SARIMAX(endog= ts_wage.loc[:test_period_start, 'Real wage'], trend='n',\n",
        "                                            order=(1,1,0), seasonal_order=(0,1,0,12)).fit()\n",
        "arima_frc =sarimax_110_010.forecast(steps = 12)\n",
        "\n",
        "#plotting the results\n",
        "plt.figure()\n",
        "plt.plot(ts_wage.loc[:test_period_start].index, ts_wage.loc[:test_period_start], label='Train')\n",
        "plt.plot(ts_wage.loc[test_period_start:].index, ts_wage.loc[test_period_start:, 'Real wage'], label='Test')\n",
        "plt.plot(arima_frc.loc[test_period_start:].index, arima_frc.loc[test_period_start:], color='green', alpha=0.7, label='SARIMAX')\n",
        "plt.title('wage forecast by sm sarimax, test period {0} = {1}'.format(loss, eval('quality' + loss)(ts_wage.loc[test_period_start:test_period_end, 'Real wage'], arima_frc.loc[test_period_start:test_period_end])[0]))\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# How to define target forecast accuracy?\n",
        "\n",
        "\n",
        "## Building Time Series Forecastability Index\n",
        "Those Time Series that have about the same accuracy level should have the same $\\color{red}{Forecastability~Index}$.\n",
        "\n",
        "\n",
        "\n",
        "<img src = 'https://github.com/aromanenko/ATSF/blob/main/pic/Forecastability%20Index.png?raw=true' width = 500pt>\n",
        "\n",
        "Techniques to do it\n",
        "* STL decomposition (detecting trend and seasonality) https://www.statsmodels.org/devel/examples/notebooks/generated/stl_decomposition.html\n",
        "\n",
        "* ABC splitting (to segment by Demand Velocity and Demand Variability) https://pypi.org/project/abc-analysis/\n",
        "\n",
        "or <a href = 'https://excel.city/2015/01/methods-of-classification-in-abc-analysis/'>more advanced tecniques </a>\n",
        "\n",
        "* Intermittency time series tests\n"
      ],
      "metadata": {
        "id": "aa2dhWgYopcx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# HW3\n",
        "* contest in kaggle: max 2 submits per day (to prevent overfitting)\n",
        "* you may use any packeages and algorithms (ML and TS)\n",
        "* 2 benchmarks:\n",
        "  - you'll get $mark \\in (6, 8]$ of 10 if your approach outperforms benchmark1\n",
        "  - you'll get $mark\\in (8,10]$ of 10 if your approach outperforms benchmark2\n",
        "  - final $mark$ will be derived based on leaderboard\n",
        "  - you have to send code with the final solution to the google form https://forms.gle/G9e9L3ue3cFhNppf7 (otherwise final mark will be 0)"
      ],
      "metadata": {
        "id": "-tzpnBzcmB-m"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZZRhwuw_MJG"
      },
      "source": [
        "# Conclusion\n",
        "## Self-check questions\n",
        "  1. Which loss-functions stimulate UnderForecasting issues?\n",
        "  2. Which loss-functions stimulate Overfrecasting issues?\n",
        "  3. What is the difference between intermittent time series and highly promo drive one?\n",
        "  4. What is the idea of Croston method?\n",
        "  5. Describe all steps in Training Time Series Forecasting Algorithims Approach\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PdjsM3cKAFX"
      },
      "source": [
        "**Materials**\n",
        "* Hyndman R.J., Athanasopoulos G. Forecasting: principles and practice,2016. https://www.otexts.org/book/fpp"
      ]
    }
  ],
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