05: Python_Functions_Time_Series_Sequencing_1.ipynb

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  "metadata": {
    "colab": {
      "name": "Python_Functions_Time_Series_Sequencing_03.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GY3HgVB7T8D"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from IPython.display import Image, display\n",
        "\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmNbWyCJ7n0a"
      },
      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MZJsxPq76CN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "outputId": "7d0d6d6e-988b-4fb5-8cb6-72e8a5d0563b"
      },
      "source": [
        "df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>Appliances</th>\n",
              "      <th>lights</th>\n",
              "      <th>T1</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>T2</th>\n",
              "      <th>RH_2</th>\n",
              "      <th>T3</th>\n",
              "      <th>RH_3</th>\n",
              "      <th>T4</th>\n",
              "      <th>RH_4</th>\n",
              "      <th>T5</th>\n",
              "      <th>RH_5</th>\n",
              "      <th>T6</th>\n",
              "      <th>RH_6</th>\n",
              "      <th>T7</th>\n",
              "      <th>RH_7</th>\n",
              "      <th>T8</th>\n",
              "      <th>RH_8</th>\n",
              "      <th>T9</th>\n",
              "      <th>RH_9</th>\n",
              "      <th>T_out</th>\n",
              "      <th>Press_mm_hg</th>\n",
              "      <th>RH_out</th>\n",
              "      <th>Windspeed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Tdewpoint</th>\n",
              "      <th>rv1</th>\n",
              "      <th>rv2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-11 17:00:00</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>47.596667</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.790000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.730000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>45.566667</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.200000</td>\n",
              "      <td>7.026667</td>\n",
              "      <td>84.256667</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.626667</td>\n",
              "      <td>18.2000</td>\n",
              "      <td>48.900000</td>\n",
              "      <td>17.033333</td>\n",
              "      <td>45.5300</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>733.5</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>5.300000</td>\n",
              "      <td>13.275433</td>\n",
              "      <td>13.275433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-11 17:10:00</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.693333</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.722500</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.790000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>45.992500</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.200000</td>\n",
              "      <td>6.833333</td>\n",
              "      <td>84.063333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.560000</td>\n",
              "      <td>18.2000</td>\n",
              "      <td>48.863333</td>\n",
              "      <td>17.066667</td>\n",
              "      <td>45.5600</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>733.6</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>59.166667</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>18.606195</td>\n",
              "      <td>18.606195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-11 17:20:00</td>\n",
              "      <td>50</td>\n",
              "      <td>30</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.626667</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>44.933333</td>\n",
              "      <td>18.926667</td>\n",
              "      <td>45.890000</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.090000</td>\n",
              "      <td>6.560000</td>\n",
              "      <td>83.156667</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.433333</td>\n",
              "      <td>18.2000</td>\n",
              "      <td>48.730000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.5000</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>733.7</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>6.333333</td>\n",
              "      <td>55.333333</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>28.642668</td>\n",
              "      <td>28.642668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-11 17:30:00</td>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.066667</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.590000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>18.890000</td>\n",
              "      <td>45.723333</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.090000</td>\n",
              "      <td>6.433333</td>\n",
              "      <td>83.423333</td>\n",
              "      <td>17.133333</td>\n",
              "      <td>41.290000</td>\n",
              "      <td>18.1000</td>\n",
              "      <td>48.590000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.4000</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>733.8</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>51.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>45.410389</td>\n",
              "      <td>45.410389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-11 17:40:00</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "      <td>19.890000</td>\n",
              "      <td>46.333333</td>\n",
              "      <td>19.200000</td>\n",
              "      <td>44.530000</td>\n",
              "      <td>19.790000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>18.890000</td>\n",
              "      <td>45.530000</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>55.090000</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>84.893333</td>\n",
              "      <td>17.200000</td>\n",
              "      <td>41.230000</td>\n",
              "      <td>18.1000</td>\n",
              "      <td>48.590000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>45.4000</td>\n",
              "      <td>6.133333</td>\n",
              "      <td>733.9</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>47.666667</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>10.084097</td>\n",
              "      <td>10.084097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>2016-05-27 17:20:00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>25.566667</td>\n",
              "      <td>46.560000</td>\n",
              "      <td>25.890000</td>\n",
              "      <td>42.025714</td>\n",
              "      <td>27.200000</td>\n",
              "      <td>41.163333</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>45.590000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>52.400000</td>\n",
              "      <td>24.796667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>44.500000</td>\n",
              "      <td>24.7000</td>\n",
              "      <td>50.074000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>755.2</td>\n",
              "      <td>55.666667</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>23.666667</td>\n",
              "      <td>13.333333</td>\n",
              "      <td>43.096812</td>\n",
              "      <td>43.096812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>2016-05-27 17:30:00</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>25.754000</td>\n",
              "      <td>42.080000</td>\n",
              "      <td>27.133333</td>\n",
              "      <td>41.223333</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>45.590000</td>\n",
              "      <td>23.230000</td>\n",
              "      <td>52.326667</td>\n",
              "      <td>24.196667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.557143</td>\n",
              "      <td>44.414286</td>\n",
              "      <td>24.7000</td>\n",
              "      <td>49.790000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>49.282940</td>\n",
              "      <td>49.282940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>2016-05-27 17:40:00</td>\n",
              "      <td>270</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.628571</td>\n",
              "      <td>42.768571</td>\n",
              "      <td>27.050000</td>\n",
              "      <td>41.690000</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>45.730000</td>\n",
              "      <td>23.230000</td>\n",
              "      <td>52.266667</td>\n",
              "      <td>23.626667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.540000</td>\n",
              "      <td>44.400000</td>\n",
              "      <td>24.7000</td>\n",
              "      <td>49.660000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.7900</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.333333</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>25.333333</td>\n",
              "      <td>13.266667</td>\n",
              "      <td>29.199117</td>\n",
              "      <td>29.199117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>2016-05-27 17:50:00</td>\n",
              "      <td>420</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>25.414000</td>\n",
              "      <td>43.036000</td>\n",
              "      <td>26.890000</td>\n",
              "      <td>41.290000</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>45.790000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>52.200000</td>\n",
              "      <td>22.433333</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>44.295714</td>\n",
              "      <td>24.6625</td>\n",
              "      <td>49.518750</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.8175</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>755.2</td>\n",
              "      <td>56.666667</td>\n",
              "      <td>3.833333</td>\n",
              "      <td>26.166667</td>\n",
              "      <td>13.233333</td>\n",
              "      <td>6.322784</td>\n",
              "      <td>6.322784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>2016-05-27 18:00:00</td>\n",
              "      <td>430</td>\n",
              "      <td>10</td>\n",
              "      <td>25.500000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>25.264286</td>\n",
              "      <td>42.971429</td>\n",
              "      <td>26.823333</td>\n",
              "      <td>41.156667</td>\n",
              "      <td>24.700000</td>\n",
              "      <td>45.963333</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>52.200000</td>\n",
              "      <td>21.026667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>44.054000</td>\n",
              "      <td>24.7360</td>\n",
              "      <td>49.736000</td>\n",
              "      <td>23.200000</td>\n",
              "      <td>46.8450</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>755.2</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>34.118851</td>\n",
              "      <td>34.118851</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19735 rows × 29 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      date  Appliances  lights  ...  Tdewpoint        rv1        rv2\n",
              "0      2016-01-11 17:00:00          60      30  ...   5.300000  13.275433  13.275433\n",
              "1      2016-01-11 17:10:00          60      30  ...   5.200000  18.606195  18.606195\n",
              "2      2016-01-11 17:20:00          50      30  ...   5.100000  28.642668  28.642668\n",
              "3      2016-01-11 17:30:00          50      40  ...   5.000000  45.410389  45.410389\n",
              "4      2016-01-11 17:40:00          60      40  ...   4.900000  10.084097  10.084097\n",
              "...                    ...         ...     ...  ...        ...        ...        ...\n",
              "19730  2016-05-27 17:20:00         100       0  ...  13.333333  43.096812  43.096812\n",
              "19731  2016-05-27 17:30:00          90       0  ...  13.300000  49.282940  49.282940\n",
              "19732  2016-05-27 17:40:00         270      10  ...  13.266667  29.199117  29.199117\n",
              "19733  2016-05-27 17:50:00         420      10  ...  13.233333   6.322784   6.322784\n",
              "19734  2016-05-27 18:00:00         430      10  ...  13.200000  34.118851  34.118851\n",
              "\n",
              "[19735 rows x 29 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoZI-S5t760x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac2e4040-ec07-459d-9a7c-d37aa8495e50"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19735 entries, 0 to 19734\n",
            "Data columns (total 29 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   date         19735 non-null  object \n",
            " 1   Appliances   19735 non-null  int64  \n",
            " 2   lights       19735 non-null  int64  \n",
            " 3   T1           19735 non-null  float64\n",
            " 4   RH_1         19735 non-null  float64\n",
            " 5   T2           19735 non-null  float64\n",
            " 6   RH_2         19735 non-null  float64\n",
            " 7   T3           19735 non-null  float64\n",
            " 8   RH_3         19735 non-null  float64\n",
            " 9   T4           19735 non-null  float64\n",
            " 10  RH_4         19735 non-null  float64\n",
            " 11  T5           19735 non-null  float64\n",
            " 12  RH_5         19735 non-null  float64\n",
            " 13  T6           19735 non-null  float64\n",
            " 14  RH_6         19735 non-null  float64\n",
            " 15  T7           19735 non-null  float64\n",
            " 16  RH_7         19735 non-null  float64\n",
            " 17  T8           19735 non-null  float64\n",
            " 18  RH_8         19735 non-null  float64\n",
            " 19  T9           19735 non-null  float64\n",
            " 20  RH_9         19735 non-null  float64\n",
            " 21  T_out        19735 non-null  float64\n",
            " 22  Press_mm_hg  19735 non-null  float64\n",
            " 23  RH_out       19735 non-null  float64\n",
            " 24  Windspeed    19735 non-null  float64\n",
            " 25  Visibility   19735 non-null  float64\n",
            " 26  Tdewpoint    19735 non-null  float64\n",
            " 27  rv1          19735 non-null  float64\n",
            " 28  rv2          19735 non-null  float64\n",
            "dtypes: float64(26), int64(2), object(1)\n",
            "memory usage: 4.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncOjFLiR0yiV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "6b1ebb54-ca18-4668-d35e-903ba1527829"
      },
      "source": [
        "df[['date','T_out', 'RH_2', 'Appliances','lights']]"
      ],
      "execution_count": 8,
      "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>date</th>\n",
              "      <th>T_out</th>\n",
              "      <th>RH_2</th>\n",
              "      <th>Appliances</th>\n",
              "      <th>lights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-01-11 17:00:00</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>44.790000</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-01-11 17:10:00</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>44.722500</td>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-01-11 17:20:00</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>44.626667</td>\n",
              "      <td>50</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-01-11 17:30:00</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>44.590000</td>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-01-11 17:40:00</td>\n",
              "      <td>6.133333</td>\n",
              "      <td>44.530000</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>2016-05-27 17:20:00</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>42.025714</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>2016-05-27 17:30:00</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>42.080000</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>2016-05-27 17:40:00</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>42.768571</td>\n",
              "      <td>270</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>2016-05-27 17:50:00</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>43.036000</td>\n",
              "      <td>420</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>2016-05-27 18:00:00</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>42.971429</td>\n",
              "      <td>430</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19735 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      date      T_out       RH_2  Appliances  lights\n",
              "0      2016-01-11 17:00:00   6.600000  44.790000          60      30\n",
              "1      2016-01-11 17:10:00   6.483333  44.722500          60      30\n",
              "2      2016-01-11 17:20:00   6.366667  44.626667          50      30\n",
              "3      2016-01-11 17:30:00   6.250000  44.590000          50      40\n",
              "4      2016-01-11 17:40:00   6.133333  44.530000          60      40\n",
              "...                    ...        ...        ...         ...     ...\n",
              "19730  2016-05-27 17:20:00  22.733333  42.025714         100       0\n",
              "19731  2016-05-27 17:30:00  22.600000  42.080000          90       0\n",
              "19732  2016-05-27 17:40:00  22.466667  42.768571         270      10\n",
              "19733  2016-05-27 17:50:00  22.333333  43.036000         420      10\n",
              "19734  2016-05-27 18:00:00  22.200000  42.971429         430      10\n",
              "\n",
              "[19735 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DX4Wjmy08aWn"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nmbda9RhEQPJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "e1def1e2-da20-4673-e198-7e4f5eeb1e5f"
      },
      "source": [
        "df.set_index('date')['T_out'].plot()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe352d997b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ag5wZCs9ZFY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "outputId": "b689a3ec-6083-4c71-d49b-2764fc958648"
      },
      "source": [
        "df.set_index('date')[['T_out', 'RH_2', 'Appliances','lights']].plot(subplots=True)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe3509ba4a8>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe34ed2e1d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe34ece11d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fe34ed181d0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayITicdCtyWr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2c12430-e39d-4f72-aca1-fa46168716c1"
      },
      "source": [
        "df.count()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date           19735\n",
              "Appliances     19735\n",
              "lights         19735\n",
              "T1             19735\n",
              "RH_1           19735\n",
              "T2             19735\n",
              "RH_2           19735\n",
              "T3             19735\n",
              "RH_3           19735\n",
              "T4             19735\n",
              "RH_4           19735\n",
              "T5             19735\n",
              "RH_5           19735\n",
              "T6             19735\n",
              "RH_6           19735\n",
              "T7             19735\n",
              "RH_7           19735\n",
              "T8             19735\n",
              "RH_8           19735\n",
              "T9             19735\n",
              "RH_9           19735\n",
              "T_out          19735\n",
              "Press_mm_hg    19735\n",
              "RH_out         19735\n",
              "Windspeed      19735\n",
              "Visibility     19735\n",
              "Tdewpoint      19735\n",
              "rv1            19735\n",
              "rv2            19735\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7iL_GfrOGwY"
      },
      "source": [
        "features=df[['Appliances','T_out', 'RH_2']].to_numpy().tolist() #more than one column so first need to convert into numpy array then into list\n",
        "target=df['Appliances'].tolist()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65R4OCK2tO6u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d7d5346-6502-4731-c2fd-020f4f46bbbe"
      },
      "source": [
        "features[0:11]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[60.0, 6.6, 44.79],\n",
              " [60.0, 6.48333333333333, 44.7225],\n",
              " [50.0, 6.36666666666667, 44.6266666666667],\n",
              " [50.0, 6.25, 44.59],\n",
              " [60.0, 6.133333333333329, 44.53],\n",
              " [50.0, 6.01666666666667, 44.5],\n",
              " [60.0, 5.9, 44.5],\n",
              " [60.0, 5.91666666666667, 44.5],\n",
              " [60.0, 5.93333333333333, 44.4333333333333],\n",
              " [70.0, 5.95, 44.4],\n",
              " [230.0, 5.96666666666667, 44.4]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GO6RNMg-tiqY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b857176-726c-4959-8944-0e692f24baa0"
      },
      "source": [
        "target[0:11]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[60, 60, 50, 50, 60, 50, 60, 60, 60, 70, 230]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMbMFqrjV6QC"
      },
      "source": [
        "!wget https://www.tensorflow.org/tutorials/structured_data/images/raw_window_1h.png\n",
        "!wget https://www.tensorflow.org/tutorials/structured_data/images/raw_window_24h.png\n",
        "!wget https://www.tensorflow.org/tutorials/structured_data/images/split_window.png"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_612h4SUoUA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "outputId": "84166f31-7697-4bee-f362-72dff0135edd"
      },
      "source": [
        "display(Image('raw_window_1h.png'))\n",
        "print(\"===========================================================================\")\n",
        "display(Image('split_window.png'))\n",
        "print(\"===========================================================================\")\n",
        "display(Image('raw_window_24h.png'))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "===========================================================================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "===========================================================================\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gtaryK2rGaQ"
      },
      "source": [
        "ts_generator = TimeseriesGenerator(features, target, length=6, sampling_rate=1, batch_size=1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gm-h1ADpwKq-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "598598c4-114e-44e0-cf2d-0f6edda4800c"
      },
      "source": [
        "ts_generator[0]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[60.        ,  6.6       , 44.79      ],\n",
              "         [60.        ,  6.48333333, 44.7225    ],\n",
              "         [50.        ,  6.36666667, 44.62666667],\n",
              "         [50.        ,  6.25      , 44.59      ],\n",
              "         [60.        ,  6.13333333, 44.53      ],\n",
              "         [50.        ,  6.01666667, 44.5       ]]]), array([60]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tArHKnLksxxI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c934a4cc-275e-46f6-f328-e7bff2c5a149"
      },
      "source": [
        "ts_generator[0][0][0].shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GWPYMHa20sF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be270ecd-6602-4f61-ed01-eabb90574ac9"
      },
      "source": [
        "ts_generator[1]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[60.        ,  6.48333333, 44.7225    ],\n",
              "         [50.        ,  6.36666667, 44.62666667],\n",
              "         [50.        ,  6.25      , 44.59      ],\n",
              "         [60.        ,  6.13333333, 44.53      ],\n",
              "         [50.        ,  6.01666667, 44.5       ],\n",
              "         [60.        ,  5.9       , 44.5       ]]]), array([60]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMBW86Pn1_yq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebf45518-8cb2-4581-cb7c-f8a088d06d11"
      },
      "source": [
        "ts_generator[2]"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[50.        ,  6.36666667, 44.62666667],\n",
              "         [50.        ,  6.25      , 44.59      ],\n",
              "         [60.        ,  6.13333333, 44.53      ],\n",
              "         [50.        ,  6.01666667, 44.5       ],\n",
              "         [60.        ,  5.9       , 44.5       ],\n",
              "         [60.        ,  5.91666667, 44.5       ]]]), array([60]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUrh59xOxdoQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58d5582e-5423-4682-fc20-3a645aa5cf64"
      },
      "source": [
        "len(ts_generator)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19729"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VAZHhpf23_x"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "Input - 1,2,3,4,5,6,7,8,9\n",
        "\n",
        "Sliding window (window size 3, stride 1) - 1,2,3    2,3,4   4,5,6\n",
        "\n",
        "Sliding window (window size 3, stride 2) - 1,2,3    3,4,5   7,8,9\n",
        "\n",
        "Tumbling window (Window size 3)   - 1,2,3   4,5,6   7,8,9\n",
        "\n",
        "\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm6QipL9xrRN"
      },
      "source": [
        "ts_generator = TimeseriesGenerator(features, target, length=6, sampling_rate=1, batch_size=1, stride=2)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cg1fvY9ux4OK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c755639-b4bf-4c32-f93b-fbb4c6a0b494"
      },
      "source": [
        "len(ts_generator)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9865"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NFA9vJ4x59L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8af1d3f-1938-4da2-c344-ad160a03a8ca"
      },
      "source": [
        "ts_generator[0]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[60.        ,  6.6       , 44.79      ],\n",
              "         [60.        ,  6.48333333, 44.7225    ],\n",
              "         [50.        ,  6.36666667, 44.62666667],\n",
              "         [50.        ,  6.25      , 44.59      ],\n",
              "         [60.        ,  6.13333333, 44.53      ],\n",
              "         [50.        ,  6.01666667, 44.5       ]]]), array([60]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sXQkfyJx_mx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b36fb02-a78d-4825-b952-41190282d811"
      },
      "source": [
        "ts_generator[1]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[50.        ,  6.36666667, 44.62666667],\n",
              "         [50.        ,  6.25      , 44.59      ],\n",
              "         [60.        ,  6.13333333, 44.53      ],\n",
              "         [50.        ,  6.01666667, 44.5       ],\n",
              "         [60.        ,  5.9       , 44.5       ],\n",
              "         [60.        ,  5.91666667, 44.5       ]]]), array([60]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbvM1xOXyDsS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "71fd3f40-1f4d-41ab-bb7c-a8c8841677b5"
      },
      "source": [
        "pd.concat([df['Appliances'],df['Appliances'].shift(-1), df['Appliances'].shift(-2)],axis=1)"
      ],
      "execution_count": 27,
      "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>Appliances</th>\n",
              "      <th>Appliances</th>\n",
              "      <th>Appliances</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>60.0</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>60</td>\n",
              "      <td>50.0</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>50.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50</td>\n",
              "      <td>60.0</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>50.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>100</td>\n",
              "      <td>90.0</td>\n",
              "      <td>270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>90</td>\n",
              "      <td>270.0</td>\n",
              "      <td>420.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>270</td>\n",
              "      <td>420.0</td>\n",
              "      <td>430.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>420</td>\n",
              "      <td>430.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>430</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19735 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Appliances  Appliances  Appliances\n",
              "0              60        60.0        50.0\n",
              "1              60        50.0        50.0\n",
              "2              50        50.0        60.0\n",
              "3              50        60.0        50.0\n",
              "4              60        50.0        60.0\n",
              "...           ...         ...         ...\n",
              "19730         100        90.0       270.0\n",
              "19731          90       270.0       420.0\n",
              "19732         270       420.0       430.0\n",
              "19733         420       430.0         NaN\n",
              "19734         430         NaN         NaN\n",
              "\n",
              "[19735 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4x06yAMT8ezk"
      },
      "source": [
        "multi_target=pd.concat([df['Appliances'],df['Appliances'].shift(-1), df['Appliances'].shift(-2)],axis=1).dropna().to_numpy().tolist()"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8R24p_d-jEo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96c52c14-fdd8-4880-cf1f-566576e50716"
      },
      "source": [
        "multi_target[0:13]"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[60.0, 60.0, 50.0],\n",
              " [60.0, 50.0, 50.0],\n",
              " [50.0, 50.0, 60.0],\n",
              " [50.0, 60.0, 50.0],\n",
              " [60.0, 50.0, 60.0],\n",
              " [50.0, 60.0, 60.0],\n",
              " [60.0, 60.0, 60.0],\n",
              " [60.0, 60.0, 70.0],\n",
              " [60.0, 70.0, 230.0],\n",
              " [70.0, 230.0, 580.0],\n",
              " [230.0, 580.0, 430.0],\n",
              " [580.0, 430.0, 250.0],\n",
              " [430.0, 250.0, 100.0]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6pM33TQ-Vvt"
      },
      "source": [
        "ts_generator = TimeseriesGenerator(features[:-2], multi_target, length=6, sampling_rate=1, batch_size=1, stride=6)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgsW6-yD-uGo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a6937bb-23ff-49db-a95e-af5c8810adac"
      },
      "source": [
        "ts_generator[0] #we will see multitarget prediction"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[60.        ,  6.6       , 44.79      ],\n",
              "         [60.        ,  6.48333333, 44.7225    ],\n",
              "         [50.        ,  6.36666667, 44.62666667],\n",
              "         [50.        ,  6.25      , 44.59      ],\n",
              "         [60.        ,  6.13333333, 44.53      ],\n",
              "         [50.        ,  6.01666667, 44.5       ]]]), array([[60., 60., 60.]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahjM0vkG-70i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3448072-3141-4b34-a2b4-f3ba3c1da58e"
      },
      "source": [
        "ts_generator[1]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[ 60.        ,   5.9       ,  44.5       ],\n",
              "         [ 60.        ,   5.91666667,  44.5       ],\n",
              "         [ 60.        ,   5.93333333,  44.43333333],\n",
              "         [ 70.        ,   5.95      ,  44.4       ],\n",
              "         [230.        ,   5.96666667,  44.4       ],\n",
              "         [580.        ,   5.98333333,  44.4       ]]]),\n",
              " array([[430., 250., 100.]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsbbA4yRgQTx"
      },
      "source": [
        "----------------------------------"
      ]
    }
  ]
}