SARIMA_multi_monthly.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SARIMA_multi_monthly.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "geqvAJuoFNLe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlvRyEB52WmW"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from sklearn import metrics\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJcpLt1G863g"
      },
      "source": [
        "data = pd.read_csv('/content/clean_monthly_df_human.csv')\n",
        "data.time=pd.to_datetime(data.time,dayfirst=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9qGp6yKGuVm",
        "outputId": "e217b897-accc-46cc-f055-1faf0d2ee88b"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['time', 'MIN_TEMPERATURE_C', 'max_clients_month',\n",
              "       'total_Holiday_Food_Related', 'total_consommation',\n",
              "       'max_pdl_total_daily_load', 'max_pdl_daily_load'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrRDeouB866S"
      },
      "source": [
        "df_input=data[['MIN_TEMPERATURE_C', 'max_clients_month',\n",
        "       'total_Holiday_Food_Related', 'total_consommation',\n",
        "       'max_pdl_total_daily_load', 'max_pdl_daily_load']]\n",
        "\n",
        "train,test=df_input[:-7],df_input[-7:]\n",
        "X_train,y_train=np.array(train.drop(['max_pdl_daily_load'],axis=1)),np.array(train.max_pdl_daily_load)\n",
        "X_test,y_test=np.array(test.drop(['max_pdl_daily_load'],axis=1)),np.array(test.max_pdl_daily_load)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rv024CQR85as",
        "outputId": "4a073cd6-40b3-495c-b456-25501f06f190"
      },
      "source": [
        "!pip install pmdarima"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pmdarima\n",
            "  Downloading pmdarima-1.8.2-cp37-cp37m-manylinux1_x86_64.whl (1.5 MB)\n",
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            "\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\n",
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            "Collecting statsmodels!=0.12.0,>=0.11\n",
            "  Downloading statsmodels-0.12.2-cp37-cp37m-manylinux1_x86_64.whl (9.5 MB)\n",
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            "Installing collected packages: statsmodels, pmdarima\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed pmdarima-1.8.2 statsmodels-0.12.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "w_bcUwZ386N9",
        "outputId": "1bea69a5-5089-4a3e-8b97-61aee699e25f"
      },
      "source": [
        "from pmdarima import auto_arima\n",
        "\n",
        "stepwise_model = auto_arima(y=y_train,X=X_train,start_p=1, start_q=1,max_p=7, max_q=7,\n",
        "                            max_P=7, max_Q=7,max_order=None,m=12,stepwise=True,\n",
        "                            seasonal=True,stationary=True,suppress_warnings=True,\n",
        "                            trace=True,)\n",
        "stepwise_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(1,0,1)(1,0,1)[12] intercept   : AIC=740.796, Time=1.05 sec\n",
            " ARIMA(0,0,0)(0,0,0)[12] intercept   : AIC=722.787, Time=0.15 sec\n",
            " ARIMA(1,0,0)(1,0,0)[12] intercept   : AIC=711.410, Time=0.55 sec\n",
            " ARIMA(0,0,1)(0,0,1)[12] intercept   : AIC=719.169, Time=0.48 sec\n",
            " ARIMA(0,0,0)(0,0,0)[12]             : AIC=907.676, Time=0.13 sec\n",
            " ARIMA(1,0,0)(0,0,0)[12] intercept   : AIC=715.442, Time=0.24 sec\n",
            " ARIMA(1,0,0)(2,0,0)[12] intercept   : AIC=712.758, Time=1.28 sec\n",
            " ARIMA(1,0,0)(1,0,1)[12] intercept   : AIC=737.911, Time=0.89 sec\n",
            " ARIMA(1,0,0)(0,0,1)[12] intercept   : AIC=709.827, Time=0.66 sec\n",
            " ARIMA(1,0,0)(0,0,2)[12] intercept   : AIC=712.154, Time=1.40 sec\n",
            " ARIMA(1,0,0)(1,0,2)[12] intercept   : AIC=714.490, Time=2.46 sec\n",
            " ARIMA(0,0,0)(0,0,1)[12] intercept   : AIC=719.862, Time=0.90 sec\n",
            " ARIMA(2,0,0)(0,0,1)[12] intercept   : AIC=703.055, Time=0.60 sec\n",
            " ARIMA(2,0,0)(0,0,0)[12] intercept   : AIC=712.681, Time=0.24 sec\n",
            " ARIMA(2,0,0)(1,0,1)[12] intercept   : AIC=706.526, Time=0.85 sec\n",
            " ARIMA(2,0,0)(0,0,2)[12] intercept   : AIC=716.444, Time=1.33 sec\n",
            " ARIMA(2,0,0)(1,0,0)[12] intercept   : AIC=702.735, Time=0.72 sec\n",
            " ARIMA(2,0,0)(2,0,0)[12] intercept   : AIC=704.648, Time=1.58 sec\n",
            " ARIMA(2,0,0)(2,0,1)[12] intercept   : AIC=708.101, Time=2.29 sec\n",
            " ARIMA(3,0,0)(1,0,0)[12] intercept   : AIC=703.843, Time=0.88 sec\n",
            " ARIMA(2,0,1)(1,0,0)[12] intercept   : AIC=705.224, Time=1.01 sec\n",
            " ARIMA(1,0,1)(1,0,0)[12] intercept   : AIC=709.496, Time=1.03 sec\n",
            " ARIMA(3,0,1)(1,0,0)[12] intercept   : AIC=705.987, Time=1.00 sec\n",
            " ARIMA(2,0,0)(1,0,0)[12]             : AIC=700.514, Time=0.98 sec\n",
            " ARIMA(2,0,0)(0,0,0)[12]             : AIC=709.172, Time=0.58 sec\n",
            " ARIMA(2,0,0)(2,0,0)[12]             : AIC=702.459, Time=1.37 sec\n",
            " ARIMA(2,0,0)(1,0,1)[12]             : AIC=702.338, Time=1.00 sec\n",
            " ARIMA(2,0,0)(0,0,1)[12]             : AIC=700.979, Time=0.64 sec\n",
            " ARIMA(2,0,0)(2,0,1)[12]             : AIC=704.985, Time=1.70 sec\n",
            " ARIMA(1,0,0)(1,0,0)[12]             : AIC=709.370, Time=0.55 sec\n",
            " ARIMA(3,0,0)(1,0,0)[12]             : AIC=701.555, Time=0.59 sec\n",
            " ARIMA(2,0,1)(1,0,0)[12]             : AIC=703.295, Time=0.75 sec\n",
            " ARIMA(1,0,1)(1,0,0)[12]             : AIC=706.263, Time=1.03 sec\n",
            " ARIMA(3,0,1)(1,0,0)[12]             : AIC=703.783, Time=0.93 sec\n",
            "\n",
            "Best model:  ARIMA(2,0,0)(1,0,0)[12]          \n",
            "Total fit time: 31.909 seconds\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>                  <td>y</td>               <th>  No. Observations:  </th>    <td>60</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(2, 0, 0)x(1, 0, 0, 12)</td> <th>  Log Likelihood     </th> <td>-341.257</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Wed, 18 Aug 2021</td>        <th>  AIC                </th>  <td>700.514</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>16:29:33</td>            <th>  BIC                </th>  <td>719.363</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th>  <td>707.887</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                              <td> - 60</td>             <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x1</th>       <td>   -1.3657</td> <td>    9.645</td> <td>   -0.142</td> <td> 0.887</td> <td>  -20.269</td> <td>   17.537</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x2</th>       <td>    0.1663</td> <td>    0.058</td> <td>    2.863</td> <td> 0.004</td> <td>    0.052</td> <td>    0.280</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x3</th>       <td>   11.0470</td> <td>    2.062</td> <td>    5.356</td> <td> 0.000</td> <td>    7.005</td> <td>   15.089</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x4</th>       <td>   -0.0003</td> <td>    0.000</td> <td>   -0.699</td> <td> 0.485</td> <td>   -0.001</td> <td>    0.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x5</th>       <td>    0.0325</td> <td>    0.007</td> <td>    4.374</td> <td> 0.000</td> <td>    0.018</td> <td>    0.047</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>    <td>    0.2170</td> <td>    0.151</td> <td>    1.439</td> <td> 0.150</td> <td>   -0.079</td> <td>    0.513</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>    <td>    0.4821</td> <td>    0.212</td> <td>    2.272</td> <td> 0.023</td> <td>    0.066</td> <td>    0.898</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L12</th> <td>    0.4805</td> <td>    0.234</td> <td>    2.052</td> <td> 0.040</td> <td>    0.022</td> <td>    0.939</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>   <td> 6618.1601</td> <td> 1776.651</td> <td>    3.725</td> <td> 0.000</td> <td> 3135.988</td> <td> 1.01e+04</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>11.80</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.94</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>1.54</td> <th>  Skew:              </th> <td>0.82</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.34</td> <th>  Kurtosis:          </th> <td>4.43</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                     SARIMAX Results                                      \n",
              "==========================================================================================\n",
              "Dep. Variable:                                  y   No. Observations:                   60\n",
              "Model:             SARIMAX(2, 0, 0)x(1, 0, 0, 12)   Log Likelihood                -341.257\n",
              "Date:                            Wed, 18 Aug 2021   AIC                            700.514\n",
              "Time:                                    16:29:33   BIC                            719.363\n",
              "Sample:                                         0   HQIC                           707.887\n",
              "                                             - 60                                         \n",
              "Covariance Type:                              opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "x1            -1.3657      9.645     -0.142      0.887     -20.269      17.537\n",
              "x2             0.1663      0.058      2.863      0.004       0.052       0.280\n",
              "x3            11.0470      2.062      5.356      0.000       7.005      15.089\n",
              "x4            -0.0003      0.000     -0.699      0.485      -0.001       0.001\n",
              "x5             0.0325      0.007      4.374      0.000       0.018       0.047\n",
              "ar.L1          0.2170      0.151      1.439      0.150      -0.079       0.513\n",
              "ar.L2          0.4821      0.212      2.272      0.023       0.066       0.898\n",
              "ar.S.L12       0.4805      0.234      2.052      0.040       0.022       0.939\n",
              "sigma2      6618.1601   1776.651      3.725      0.000    3135.988    1.01e+04\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):                11.80\n",
              "Prob(Q):                              0.94   Prob(JB):                         0.00\n",
              "Heteroskedasticity (H):               1.54   Skew:                             0.82\n",
              "Prob(H) (two-sided):                  0.34   Kurtosis:                         4.43\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKUV_Vyg9b9P"
      },
      "source": [
        "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
        "  def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "  def smape(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return 2/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum((np.abs(y_true)+np.abs(y_pred)))*100\n",
        "  def mase(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    naive_pred=y_true[1:]\n",
        "    return 1/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum(np.abs(y_true[:len(y_true)-1]-naive_pred))*(y_true.shape[0]-1)\n",
        "  #def owe(y_true,y_pred):\n",
        "   # return (mase(y_true,y_pred)+smape(y_true,y_pred))/2\n",
        "\n",
        "  print('Evaluation metric results:-')\n",
        "  print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')\n",
        "  print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')\n",
        "  print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')\n",
        "  print(f'MAPE is : {mean_absolute_percentage_error(y_true, y_pred)} %')\n",
        "  #mape.append(mean_absolute_percentage_error(y_true, y_pred))\n",
        "  #rmse.append(np.sqrt(metrics.mean_squared_error(y_true, y_pred)))\n",
        "  print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\\n\\n')\n",
        "  print('SMAPE is : '+str(smape(y_true,y_pred))+'\\n')\n",
        "  print('MASE is : '+str(mase(y_true,y_pred)))\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "K0ZseVHaKVP9",
        "outputId": "a38a92ad-fc15-4a76-cb6b-b995a103ef8f"
      },
      "source": [
        "test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MIN_TEMPERATURE_C</th>\n",
              "      <th>max_clients_month</th>\n",
              "      <th>total_Holiday_Food_Related</th>\n",
              "      <th>total_consommation</th>\n",
              "      <th>max_pdl_total_daily_load</th>\n",
              "      <th>max_pdl_daily_load</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>8</td>\n",
              "      <td>5339</td>\n",
              "      <td>1</td>\n",
              "      <td>249158.97</td>\n",
              "      <td>26139</td>\n",
              "      <td>1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>11</td>\n",
              "      <td>5348</td>\n",
              "      <td>0</td>\n",
              "      <td>222261.04</td>\n",
              "      <td>23365</td>\n",
              "      <td>1451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>9</td>\n",
              "      <td>5321</td>\n",
              "      <td>0</td>\n",
              "      <td>219707.61</td>\n",
              "      <td>23370</td>\n",
              "      <td>1463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>12</td>\n",
              "      <td>5324</td>\n",
              "      <td>7</td>\n",
              "      <td>207015.41</td>\n",
              "      <td>18875</td>\n",
              "      <td>1599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>18</td>\n",
              "      <td>5326</td>\n",
              "      <td>26</td>\n",
              "      <td>193552.21</td>\n",
              "      <td>15439</td>\n",
              "      <td>1400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>20</td>\n",
              "      <td>5324</td>\n",
              "      <td>0</td>\n",
              "      <td>182548.79</td>\n",
              "      <td>14697</td>\n",
              "      <td>1105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>23</td>\n",
              "      <td>5277</td>\n",
              "      <td>1</td>\n",
              "      <td>177683.15</td>\n",
              "      <td>13664</td>\n",
              "      <td>1084</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    MIN_TEMPERATURE_C  ...  max_pdl_daily_load\n",
              "60                  8  ...                1560\n",
              "61                 11  ...                1451\n",
              "62                  9  ...                1463\n",
              "63                 12  ...                1599\n",
              "64                 18  ...                1400\n",
              "65                 20  ...                1105\n",
              "66                 23  ...                1084\n",
              "\n",
              "[7 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XQLv8RSPooa"
      },
      "source": [
        "y_test=pd.DataFrame(data=y_test,index=np.arange(60,67),columns=['actual'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXMU0v0I9cAI",
        "outputId": "a300e205-d8bc-4d16-86a5-6a782d50f703"
      },
      "source": [
        "forecast,conf_int = stepwise_model.predict(n_periods=7, X=X_test, return_conf_int=True)\n",
        "#alpha=confidence_interval=0.05 (previous method)\n",
        "forecast = pd.DataFrame(forecast,columns=['PDL_pred'])\n",
        "df_conf = pd.DataFrame(conf_int,columns= ['Upper_bound','Lower_bound'])\n",
        "df_conf[\"new_index\"] = range(60, 67)\n",
        "df_conf = df_conf.set_index(\"new_index\")\n",
        "timeseries_evaluation_metrics_func(y_test.actual, forecast.PDL_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 12773.40054719756\n",
            "MAE is : 89.0517492694256\n",
            "RMSE is : 113.01946977046724\n",
            "MAPE is : 6.8727163566025835 %\n",
            "R2 is : 0.6506337485416404\n",
            "\n",
            "SMAPE is : 0.9072458464728825\n",
            "\n",
            "MASE is : 0.692112040954085\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oT1rpoUj9cCr"
      },
      "source": [
        "forecast[\"new_index\"] = range(60, 67)\n",
        "forecast = forecast.set_index(\"new_index\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "EPrvKKOp9cEv",
        "outputId": "9e834100-3563-41ae-82c2-8c74db405369"
      },
      "source": [
        "forecast"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PDL_pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>new_index</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1564.780325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>1461.624358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>1501.315888</td>\n",
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              "      <th>63</th>\n",
              "      <td>1440.932762</td>\n",
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              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>1547.587326</td>\n",
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              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>1292.662669</td>\n",
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              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>1160.324441</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              PDL_pred\n",
              "new_index             \n",
              "60         1564.780325\n",
              "61         1461.624358\n",
              "62         1501.315888\n",
              "63         1440.932762\n",
              "64         1547.587326\n",
              "65         1292.662669\n",
              "66         1160.324441"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "9EUKZF4U_r_d",
        "outputId": "935b2163-62c0-4439-f1d9-9f6a1dfb5680"
      },
      "source": [
        "get_ipython().run_line_magic('matplotlib', 'inline')\n",
        "plt.rcParams[\"figure.figsize\"] = [15,7]\n",
        "plt.plot(y_train, label='Train ')\n",
        "plt.plot(y_test.actual, label='Test ')\n",
        "plt.plot(forecast['PDL_pred'], label='Predicted ')\n",
        "plt.plot(df_conf['Upper_bound'], label='Confidence Interval Upper bound ')\n",
        "plt.plot(df_conf['Lower_bound'], label='Confidence Interval Lower bound ')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "YuZHCV9i_Muz",
        "outputId": "cd74a635-c397-4619-9d06-d44ae1fbd317"
      },
      "source": [
        "# plot baseline and predictions\n",
        "time=data.time[-7:]\n",
        "\n",
        "plt.plot(time,y_test.actual,label='actual')\n",
        "plt.plot(time,forecast['PDL_pred'],label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iB0zI574N5jv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boWo2CvQGkkd"
      },
      "source": [
        "# peak period"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hIgVR90Glba",
        "outputId": "9984c75b-edac-4b60-b299-dd8bbe1087a6"
      },
      "source": [
        "timeseries_evaluation_metrics_func(y_test.actual[:3], forecast.PDL_pred[:3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 534.6119184974157\n",
            "MAE is : 17.906856982121855\n",
            "RMSE is : 23.121676377317794\n",
            "MAPE is : 1.2192116174419647 %\n",
            "R2 is : 0.7755198625325772\n",
            "\n",
            "SMAPE is : 0.39785409558073587\n",
            "\n",
            "MASE is : 0.29598110714251\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "2hiGoxFeGmJN",
        "outputId": "69adf821-a3d8-47e5-df27-c1542cf235f1"
      },
      "source": [
        "plt.plot(time[:3],y_test.actual[:3],label='actual')\n",
        "plt.plot(time[:3],forecast['PDL_pred'][:3],label='forecast')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FtIAUtmGzLy"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}