Auto_ML_Time_Series_Forecasting.ipynb

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      "name": "Auto ML - Time Series Forecasting.ipynb",
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      "source": [
        "pip install pycaret"
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            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "6lZHM6IeU18P",
        "outputId": "a7de95d7-c4a5-478b-c8d4-e8693b46068b"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-93c054c5-1949-4b30-88c0-e1365563ecb5\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-93c054c5-1949-4b30-88c0-e1365563ecb5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving anomaly detection vnindex.xls to anomaly detection vnindex.xls\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9vdUZXjVSg6",
        "outputId": "de790d54-2f03-4770-ecce-adfc6e23779f"
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "data = pd.read_excel(io.BytesIO(uploaded['anomaly detection vnindex.xls']))\n",
        "print(data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*** No CODEPAGE record, no encoding_override: will use 'ascii'\n",
            "            Date      Time        Close\n",
            "0     2021/07/08  09:00:00  1374.680054\n",
            "1     2021/07/07  09:00:00  1388.550000\n",
            "2     2021/07/06  09:00:00  1354.790000\n",
            "3     2021/07/05  09:00:00  1411.130000\n",
            "4     2021/07/02  09:00:00  1420.270000\n",
            "...          ...       ...          ...\n",
            "2997  2009/07/07  09:00:00   447.630000\n",
            "2998  2009/07/06  09:00:00   454.270000\n",
            "2999  2009/07/03  09:00:00   435.440000\n",
            "3000  2009/07/02  09:00:00   433.790000\n",
            "3001  2009/07/01  09:00:00   430.000000\n",
            "\n",
            "[3002 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "c0qXCTPBWaxz",
        "outputId": "803ece4f-6b56-4787-ae1e-7cf6031c4667"
      },
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'])\n",
        "data = data.drop(['Time'],axis = 1)\n",
        "data = data.dropna()\n",
        "data"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-07-08</td>\n",
              "      <td>1374.680054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-07-07</td>\n",
              "      <td>1388.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-07-06</td>\n",
              "      <td>1354.790000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>1411.130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>1420.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2009-07-07</td>\n",
              "      <td>447.630000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2009-07-06</td>\n",
              "      <td>454.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>2009-07-03</td>\n",
              "      <td>435.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3000</th>\n",
              "      <td>2009-07-02</td>\n",
              "      <td>433.790000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3001</th>\n",
              "      <td>2009-07-01</td>\n",
              "      <td>430.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3002 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date        Close\n",
              "0    2021-07-08  1374.680054\n",
              "1    2021-07-07  1388.550000\n",
              "2    2021-07-06  1354.790000\n",
              "3    2021-07-05  1411.130000\n",
              "4    2021-07-02  1420.270000\n",
              "...         ...          ...\n",
              "2997 2009-07-07   447.630000\n",
              "2998 2009-07-06   454.270000\n",
              "2999 2009-07-03   435.440000\n",
              "3000 2009-07-02   433.790000\n",
              "3001 2009-07-01   430.000000\n",
              "\n",
              "[3002 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "7cIsECRcWcsC",
        "outputId": "602880f9-f813-4f7c-80a8-4b98ed85d533"
      },
      "source": [
        "import numpy as np\n",
        "# extract month and year from dates\n",
        "data['Month'] = [i.month for i in data['Date']]\n",
        "data['Year'] = [i.year for i in data['Date']]\n",
        "# create a sequence of numbers\n",
        "data['Series'] = np.arange(1,len(data)+1)\n",
        "# drop unnecessary columns and re-arrange\n",
        "data.drop(['Date'], axis=1, inplace=True)\n",
        "data = data[['Series', 'Year', 'Month', 'Close']] \n",
        "# check the head of the dataset\n",
        "data.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>Series</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>7</td>\n",
              "      <td>1374.680054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "      <td>7</td>\n",
              "      <td>1388.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2021</td>\n",
              "      <td>7</td>\n",
              "      <td>1354.790000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2021</td>\n",
              "      <td>7</td>\n",
              "      <td>1411.130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2021</td>\n",
              "      <td>7</td>\n",
              "      <td>1420.270000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Series  Year  Month        Close\n",
              "0       1  2021      7  1374.680054\n",
              "1       2  2021      7  1388.550000\n",
              "2       3  2021      7  1354.790000\n",
              "3       4  2021      7  1411.130000\n",
              "4       5  2021      7  1420.270000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2EvCieCWmDH",
        "outputId": "5875f5ec-e31f-47c1-e9e9-64e8f688caab"
      },
      "source": [
        "# split data into train-test set\n",
        "train = data[data['Year'] < 2020]\n",
        "test = data[data['Year'] >= 2020]\n",
        "# check shape\n",
        "train.shape, test.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2624, 4), (378, 4))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzn3K9l9Xar_"
      },
      "source": [
        "# import the regression module\n",
        "from pycaret.regression import *\n",
        "# initialize setup\n",
        "s = setup(data = train, test_data = test, target = 'Close', fold_strategy = 'timeseries', numeric_features = ['Year', 'Series'], fold = 3, transform_target = True, session_id = 123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550,
          "referenced_widgets": [
            "da15736bf73641e19f8763b594de8719",
            "758c9f5ba1cb4a3db97785f200ab8f05",
            "a06903d1afef44b1a5cadba3d8f21d79"
          ]
        },
        "id": "Iy6_sUCtXfQ9",
        "outputId": "ba7bfd79-2b40-48be-d04e-f5769b35df3f"
      },
      "source": [
        "best = compare_models(sort = 'MAE')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>omp</th>\n",
              "      <td>Orthogonal Matching Pursuit</td>\n",
              "      <td>107.7934</td>\n",
              "      <td>16715.4831</td>\n",
              "      <td>118.7870</td>\n",
              "      <td>-4.3746</td>\n",
              "      <td>0.2222</td>\n",
              "      <td>0.1961</td>\n",
              "      <td>0.0233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>112.2195</td>\n",
              "      <td>19887.8225</td>\n",
              "      <td>130.3030</td>\n",
              "      <td>-5.0066</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.2129</td>\n",
              "      <td>0.2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>113.9584</td>\n",
              "      <td>20409.3056</td>\n",
              "      <td>131.4294</td>\n",
              "      <td>-5.1979</td>\n",
              "      <td>0.2265</td>\n",
              "      <td>0.2150</td>\n",
              "      <td>0.0233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>114.2505</td>\n",
              "      <td>20478.6020</td>\n",
              "      <td>131.7657</td>\n",
              "      <td>-5.2079</td>\n",
              "      <td>0.2271</td>\n",
              "      <td>0.2159</td>\n",
              "      <td>0.4667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>AdaBoost Regressor</td>\n",
              "      <td>116.7446</td>\n",
              "      <td>21061.2216</td>\n",
              "      <td>132.1314</td>\n",
              "      <td>-5.3554</td>\n",
              "      <td>0.2255</td>\n",
              "      <td>0.2211</td>\n",
              "      <td>0.0600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbr</th>\n",
              "      <td>Gradient Boosting Regressor</td>\n",
              "      <td>124.9498</td>\n",
              "      <td>24765.8176</td>\n",
              "      <td>141.1001</td>\n",
              "      <td>-6.5767</td>\n",
              "      <td>0.2381</td>\n",
              "      <td>0.2338</td>\n",
              "      <td>0.0800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Regressor</td>\n",
              "      <td>125.1249</td>\n",
              "      <td>25289.2309</td>\n",
              "      <td>143.0973</td>\n",
              "      <td>-6.7323</td>\n",
              "      <td>0.2411</td>\n",
              "      <td>0.2344</td>\n",
              "      <td>0.3967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>br</th>\n",
              "      <td>Bayesian Ridge</td>\n",
              "      <td>126.5189</td>\n",
              "      <td>24403.1653</td>\n",
              "      <td>139.5713</td>\n",
              "      <td>-6.8237</td>\n",
              "      <td>0.2495</td>\n",
              "      <td>0.2282</td>\n",
              "      <td>0.0267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Regression</td>\n",
              "      <td>126.8562</td>\n",
              "      <td>24573.5805</td>\n",
              "      <td>140.0090</td>\n",
              "      <td>-6.8777</td>\n",
              "      <td>0.2501</td>\n",
              "      <td>0.2288</td>\n",
              "      <td>0.0233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lar</th>\n",
              "      <td>Least Angle Regression</td>\n",
              "      <td>127.1401</td>\n",
              "      <td>24729.9127</td>\n",
              "      <td>140.3848</td>\n",
              "      <td>-6.9275</td>\n",
              "      <td>0.2505</td>\n",
              "      <td>0.2293</td>\n",
              "      <td>0.0267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>127.1426</td>\n",
              "      <td>24730.6659</td>\n",
              "      <td>140.3868</td>\n",
              "      <td>-6.9277</td>\n",
              "      <td>0.2505</td>\n",
              "      <td>0.2293</td>\n",
              "      <td>0.9500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Regressor</td>\n",
              "      <td>142.4903</td>\n",
              "      <td>32032.1148</td>\n",
              "      <td>160.3357</td>\n",
              "      <td>-8.6924</td>\n",
              "      <td>0.2615</td>\n",
              "      <td>0.2647</td>\n",
              "      <td>0.0867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>en</th>\n",
              "      <td>Elastic Net</td>\n",
              "      <td>163.6007</td>\n",
              "      <td>44148.5348</td>\n",
              "      <td>173.2759</td>\n",
              "      <td>-12.9420</td>\n",
              "      <td>0.2751</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>0.0267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lasso</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>184.6979</td>\n",
              "      <td>50007.6930</td>\n",
              "      <td>193.8568</td>\n",
              "      <td>-14.0891</td>\n",
              "      <td>0.3052</td>\n",
              "      <td>0.3436</td>\n",
              "      <td>0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>llar</th>\n",
              "      <td>Lasso Least Angle Regression</td>\n",
              "      <td>260.1398</td>\n",
              "      <td>75518.5161</td>\n",
              "      <td>267.2879</td>\n",
              "      <td>-20.7278</td>\n",
              "      <td>0.4201</td>\n",
              "      <td>0.5101</td>\n",
              "      <td>0.0233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>huber</th>\n",
              "      <td>Huber Regressor</td>\n",
              "      <td>266.7900</td>\n",
              "      <td>80983.7240</td>\n",
              "      <td>275.4945</td>\n",
              "      <td>-22.5004</td>\n",
              "      <td>0.4284</td>\n",
              "      <td>0.5214</td>\n",
              "      <td>0.0400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>par</th>\n",
              "      <td>Passive Aggressive Regressor</td>\n",
              "      <td>279.7961</td>\n",
              "      <td>107273.2366</td>\n",
              "      <td>287.5799</td>\n",
              "      <td>-26.9314</td>\n",
              "      <td>0.4228</td>\n",
              "      <td>0.5418</td>\n",
              "      <td>0.0233</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model       MAE          MSE      RMSE  \\\n",
              "omp           Orthogonal Matching Pursuit  107.7934   16715.4831  118.7870   \n",
              "lightgbm  Light Gradient Boosting Machine  112.2195   19887.8225  130.3030   \n",
              "dt                Decision Tree Regressor  113.9584   20409.3056  131.4294   \n",
              "rf                Random Forest Regressor  114.2505   20478.6020  131.7657   \n",
              "ada                    AdaBoost Regressor  116.7446   21061.2216  132.1314   \n",
              "gbr           Gradient Boosting Regressor  124.9498   24765.8176  141.1001   \n",
              "et                  Extra Trees Regressor  125.1249   25289.2309  143.0973   \n",
              "br                         Bayesian Ridge  126.5189   24403.1653  139.5713   \n",
              "ridge                    Ridge Regression  126.8562   24573.5805  140.0090   \n",
              "lar                Least Angle Regression  127.1401   24729.9127  140.3848   \n",
              "lr                      Linear Regression  127.1426   24730.6659  140.3868   \n",
              "knn                 K Neighbors Regressor  142.4903   32032.1148  160.3357   \n",
              "en                            Elastic Net  163.6007   44148.5348  173.2759   \n",
              "lasso                    Lasso Regression  184.6979   50007.6930  193.8568   \n",
              "llar         Lasso Least Angle Regression  260.1398   75518.5161  267.2879   \n",
              "huber                     Huber Regressor  266.7900   80983.7240  275.4945   \n",
              "par          Passive Aggressive Regressor  279.7961  107273.2366  287.5799   \n",
              "\n",
              "               R2   RMSLE    MAPE  TT (Sec)  \n",
              "omp       -4.3746  0.2222  0.1961    0.0233  \n",
              "lightgbm  -5.0066  0.2248  0.2129    0.2100  \n",
              "dt        -5.1979  0.2265  0.2150    0.0233  \n",
              "rf        -5.2079  0.2271  0.2159    0.4667  \n",
              "ada       -5.3554  0.2255  0.2211    0.0600  \n",
              "gbr       -6.5767  0.2381  0.2338    0.0800  \n",
              "et        -6.7323  0.2411  0.2344    0.3967  \n",
              "br        -6.8237  0.2495  0.2282    0.0267  \n",
              "ridge     -6.8777  0.2501  0.2288    0.0233  \n",
              "lar       -6.9275  0.2505  0.2293    0.0267  \n",
              "lr        -6.9277  0.2505  0.2293    0.9500  \n",
              "knn       -8.6924  0.2615  0.2647    0.0867  \n",
              "en       -12.9420  0.2751  0.2965    0.0267  \n",
              "lasso    -14.0891  0.3052  0.3436    0.0300  \n",
              "llar     -20.7278  0.4201  0.5101    0.0233  \n",
              "huber    -22.5004  0.4284  0.5214    0.0400  \n",
              "par      -26.9314  0.4228  0.5418    0.0233  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        },
        "id": "WRWAAWgkXqZf",
        "outputId": "f9fde268-0d9b-48ed-9e1e-cb8d16dcd4fd"
      },
      "source": [
        "prediction_holdout = predict_model(best)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .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>Model</th>\n",
              "      <th>MAE</th>\n",
              "      <th>MSE</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>R2</th>\n",
              "      <th>RMSLE</th>\n",
              "      <th>MAPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Orthogonal Matching Pursuit</td>\n",
              "      <td>136.8618</td>\n",
              "      <td>26392.4012</td>\n",
              "      <td>162.4574</td>\n",
              "      <td>0.2436</td>\n",
              "      <td>0.1713</td>\n",
              "      <td>0.1538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Model       MAE         MSE  ...      R2   RMSLE    MAPE\n",
              "0  Orthogonal Matching Pursuit  136.8618  26392.4012  ...  0.2436  0.1713  0.1538\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQbgw5qtXw0L"
      },
      "source": [
        "final_best = finalize_model(best)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "NsG_HEwpYhQU",
        "outputId": "dc9f8310-268f-4f2b-e89a-1f19f7babf8c"
      },
      "source": [
        "future_dates = pd.date_range(start = '2009-07-01', end = '2021-07-08', freq = 'MS')\n",
        "future_df = pd.DataFrame()\n",
        "future_df['Month'] = [i.month for i in future_dates]\n",
        "future_df['Year'] = [i.year for i in future_dates]    \n",
        "future_df['Series'] = np.arange(145,(145+len(future_dates)))\n",
        "future_df.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Series</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>2009</td>\n",
              "      <td>145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>2009</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>2009</td>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>2009</td>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>2009</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Month  Year  Series\n",
              "0      7  2009     145\n",
              "1      8  2009     146\n",
              "2      9  2009     147\n",
              "3     10  2009     148\n",
              "4     11  2009     149"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "dk4KfeYuY3KR",
        "outputId": "9e198a49-64e0-42ff-c900-66a71496de1b"
      },
      "source": [
        "predictions_future = predict_model(final_best, data=future_df)\n",
        "predictions_future.tail(10)"
      ],
      "execution_count": 25,
      "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>Month</th>\n",
              "      <th>Year</th>\n",
              "      <th>Series</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>10</td>\n",
              "      <td>2020</td>\n",
              "      <td>280</td>\n",
              "      <td>1054.693900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>11</td>\n",
              "      <td>2020</td>\n",
              "      <td>281</td>\n",
              "      <td>1054.693900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>12</td>\n",
              "      <td>2020</td>\n",
              "      <td>282</td>\n",
              "      <td>1054.693900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>1</td>\n",
              "      <td>2021</td>\n",
              "      <td>283</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>2</td>\n",
              "      <td>2021</td>\n",
              "      <td>284</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>3</td>\n",
              "      <td>2021</td>\n",
              "      <td>285</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>4</td>\n",
              "      <td>2021</td>\n",
              "      <td>286</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>5</td>\n",
              "      <td>2021</td>\n",
              "      <td>287</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>6</td>\n",
              "      <td>2021</td>\n",
              "      <td>288</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>7</td>\n",
              "      <td>2021</td>\n",
              "      <td>289</td>\n",
              "      <td>1225.475906</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Month  Year  Series        Label\n",
              "135     10  2020     280  1054.693900\n",
              "136     11  2020     281  1054.693900\n",
              "137     12  2020     282  1054.693900\n",
              "138      1  2021     283  1225.475906\n",
              "139      2  2021     284  1225.475906\n",
              "140      3  2021     285  1225.475906\n",
              "141      4  2021     286  1225.475906\n",
              "142      5  2021     287  1225.475906\n",
              "143      6  2021     288  1225.475906\n",
              "144      7  2021     289  1225.475906"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8N5xMv5cY_8H"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}