Multivariate_Time_Series_Modeling_using_LSTM.ipynb

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    "colab": {
      "name": "Multivariate Time Series Modeling using LSTM.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP9xZLy1nz/eW6c0Yy8dif0",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/End-to-End-Time-Series/blob/master/Multivariate_Time_Series_Modeling_using_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GY3HgVB7T8D"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "import tensorflow as tf\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MZJsxPq76CN",
        "outputId": "70a9c286-7397-4692-80e3-3276025eb043",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        }
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "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",
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              "  </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",
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              "    <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",
              "      <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",
              "    </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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xYLENkY8Pdv"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "Attribute Information:\n",
        "\n",
        "date time year-month-day hour:minute:second\n",
        "Appliances, energy use in Wh\n",
        "lights, energy use of light fixtures in the house in Wh\n",
        "T1, Temperature in kitchen area, in Celsius\n",
        "RH_1, Humidity in kitchen area, in %\n",
        "T2, Temperature in living room area, in Celsius\n",
        "RH_2, Humidity in living room area, in %\n",
        "T3, Temperature in laundry room area\n",
        "RH_3, Humidity in laundry room area, in %\n",
        "T4, Temperature in office room, in Celsius\n",
        "RH_4, Humidity in office room, in %\n",
        "T5, Temperature in bathroom, in Celsius\n",
        "RH_5, Humidity in bathroom, in %\n",
        "T6, Temperature outside the building (north side), in Celsius\n",
        "RH_6, Humidity outside the building (north side), in %\n",
        "T7, Temperature in ironing room , in Celsius\n",
        "RH_7, Humidity in ironing room, in %\n",
        "T8, Temperature in teenager room 2, in Celsius\n",
        "RH_8, Humidity in teenager room 2, in %\n",
        "T9, Temperature in parents room, in Celsius\n",
        "RH_9, Humidity in parents room, in %\n",
        "To, Temperature outside (from Chievres weather station), in Celsius\n",
        "Pressure (from Chievres weather station), in mm Hg\n",
        "RH_out, Humidity outside (from Chievres weather station), in %\n",
        "Wind speed (from Chievres weather station), in m/s\n",
        "Visibility (from Chievres weather station), in km\n",
        "Tdewpoint (from Chievres weather station), Â°C\n",
        "rv1, Random variable 1, nondimensional\n",
        "rv2, Random variable 2, nondimensional\n",
        "\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_gfpzvA0cJa",
        "outputId": "4583dc1e-5f68-482d-9eed-4adb166dc0de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "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  datetime64[ns]\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: datetime64[ns](1), float64(26), int64(2)\n",
            "memory usage: 4.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DX4Wjmy08aWn"
      },
      "source": [
        "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6klgmpWq0TJZ",
        "outputId": "8db62616-a14a-4bd2-dfda-4fd6d2e61ddc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        }
      },
      "source": [
        "df.set_index('date')[['Appliances', 'lights','T_out', 'RH_1', 'Visibility']].plot(subplots=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6515970cf8>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6515502f98>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f65154bcf98>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6515471f98>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6515427f98>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03LtErv2z8fI"
      },
      "source": [
        "df_input=df[['Appliances','T_out', 'RH_1', 'Visibility']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoZI-S5t760x",
        "outputId": "1bf45620-ab12-426b-fe4e-1efcdec3d43c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "df_input"
      ],
      "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>Appliances</th>\n",
              "      <th>T_out</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>Visibility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>60</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>47.596667</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>60</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>46.693333</td>\n",
              "      <td>59.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>55.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>46.066667</td>\n",
              "      <td>51.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>6.133333</td>\n",
              "      <td>46.333333</td>\n",
              "      <td>47.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19730</th>\n",
              "      <td>100</td>\n",
              "      <td>22.733333</td>\n",
              "      <td>46.560000</td>\n",
              "      <td>23.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19731</th>\n",
              "      <td>90</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>24.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19732</th>\n",
              "      <td>270</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>420</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>26.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>430</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19735 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility\n",
              "0              60   6.600000  47.596667   63.000000\n",
              "1              60   6.483333  46.693333   59.166667\n",
              "2              50   6.366667  46.300000   55.333333\n",
              "3              50   6.250000  46.066667   51.500000\n",
              "4              60   6.133333  46.333333   47.666667\n",
              "...           ...        ...        ...         ...\n",
              "19730         100  22.733333  46.560000   23.666667\n",
              "19731          90  22.600000  46.500000   24.500000\n",
              "19732         270  22.466667  46.596667   25.333333\n",
              "19733         420  22.333333  46.990000   26.166667\n",
              "19734         430  22.200000  46.600000   27.000000\n",
              "\n",
              "[19735 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3QqtUCsyKLa",
        "outputId": "246287b9-2ca9-431f-aef6-0922beccec2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "df_input.describe()"
      ],
      "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>Appliances</th>\n",
              "      <th>T_out</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>Visibility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "      <td>19735.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>97.694958</td>\n",
              "      <td>7.411665</td>\n",
              "      <td>40.259739</td>\n",
              "      <td>38.330834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>102.524891</td>\n",
              "      <td>5.317409</td>\n",
              "      <td>3.979299</td>\n",
              "      <td>11.794719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>-5.000000</td>\n",
              "      <td>27.023333</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>37.333333</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>6.916667</td>\n",
              "      <td>39.656667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>10.408333</td>\n",
              "      <td>43.066667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1080.000000</td>\n",
              "      <td>26.100000</td>\n",
              "      <td>63.360000</td>\n",
              "      <td>66.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Appliances         T_out          RH_1    Visibility\n",
              "count  19735.000000  19735.000000  19735.000000  19735.000000\n",
              "mean      97.694958      7.411665     40.259739     38.330834\n",
              "std      102.524891      5.317409      3.979299     11.794719\n",
              "min       10.000000     -5.000000     27.023333      1.000000\n",
              "25%       50.000000      3.666667     37.333333     29.000000\n",
              "50%       60.000000      6.916667     39.656667     40.000000\n",
              "75%      100.000000     10.408333     43.066667     40.000000\n",
              "max     1080.000000     26.100000     63.360000     66.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_p2XsCLfyW4C",
        "outputId": "3d8d5dfd-656e-463a-8338-b91b0725e829",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
      },
      "source": [
        "df_input.query(\"Appliances > 500\")"
      ],
      "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",
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              "\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>T_out</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>Visibility</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>580</td>\n",
              "      <td>5.983333</td>\n",
              "      <td>46.396667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>520</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>41.530000</td>\n",
              "      <td>36.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>550</td>\n",
              "      <td>2.033333</td>\n",
              "      <td>43.433333</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>690</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>43.766667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>620</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>43.693333</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19586</th>\n",
              "      <td>600</td>\n",
              "      <td>20.833333</td>\n",
              "      <td>44.066667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19587</th>\n",
              "      <td>520</td>\n",
              "      <td>20.900000</td>\n",
              "      <td>43.366667</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19684</th>\n",
              "      <td>580</td>\n",
              "      <td>15.900000</td>\n",
              "      <td>47.226667</td>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19685</th>\n",
              "      <td>660</td>\n",
              "      <td>16.100000</td>\n",
              "      <td>47.693333</td>\n",
              "      <td>61.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19695</th>\n",
              "      <td>620</td>\n",
              "      <td>19.100000</td>\n",
              "      <td>48.326667</td>\n",
              "      <td>64.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>288 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility\n",
              "11            580   5.983333  46.396667   40.000000\n",
              "314           520   2.900000  41.530000   36.333333\n",
              "392           550   2.033333  43.433333   40.000000\n",
              "393           690   2.100000  43.766667   40.000000\n",
              "394           620   2.166667  43.693333   40.000000\n",
              "...           ...        ...        ...         ...\n",
              "19586         600  20.833333  44.066667   40.000000\n",
              "19587         520  20.900000  43.366667   40.000000\n",
              "19684         580  15.900000  47.226667   61.000000\n",
              "19685         660  16.100000  47.693333   61.000000\n",
              "19695         620  19.100000  48.326667   64.000000\n",
              "\n",
              "[288 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdnkSpWaENYQ"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "data_scaled = scaler.fit_transform(df_input)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCY48qWVESHY",
        "outputId": "60ff81c6-3466-4318-ed51-b1834e4c20a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "data_scaled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04672897, 0.37299035, 0.56618659, 0.95384615],\n",
              "       [0.04672897, 0.36923901, 0.54132648, 0.89487179],\n",
              "       [0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "       ...,\n",
              "       [0.24299065, 0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.38317757, 0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.39252336, 0.87459807, 0.53875791, 0.4       ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEZuMY99zfLU"
      },
      "source": [
        "features=data_scaled\n",
        "target=data_scaled[:,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Ag5wZCs9ZFY",
        "outputId": "765a49c2-17c3-4951-8acb-953568ce208f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "TimeseriesGenerator(features, target, length=2, sampling_rate=1, batch_size=1)[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[0.04672897, 0.37299035, 0.56618659, 0.95384615],\n",
              "         [0.04672897, 0.36923901, 0.54132648, 0.89487179]]]),\n",
              " array([0.03738318]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3D7t9UA4Juf"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=123, shuffle = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLv-nf0rFlLw",
        "outputId": "7e8111e6-467c-4b22-ccdf-0258ee2479fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15788, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ez0G67QwK_aW",
        "outputId": "cf6ee7d0-7315-4cf3-dcab-431692999d67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3947, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAR9vd-n1MIK"
      },
      "source": [
        "win_length=720\n",
        "batch_size=32\n",
        "num_features=4\n",
        "train_generator = TimeseriesGenerator(x_train, y_train, length=win_length, sampling_rate=1, batch_size=batch_size)\n",
        "test_generator = TimeseriesGenerator(x_test, y_test, length=win_length, sampling_rate=1, batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YW4H0bXV2NwA",
        "outputId": "c084f7c9-4cb6-4118-af77-f3a1882825db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_generator[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[[0.04672897, 0.37299035, 0.56618659, 0.95384615],\n",
              "         [0.04672897, 0.36923901, 0.54132648, 0.89487179],\n",
              "         [0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "         ...,\n",
              "         [0.06542056, 0.25241158, 0.31584258, 0.79230769],\n",
              "         [0.05607477, 0.23901393, 0.31309054, 0.72820513],\n",
              "         [0.06542056, 0.22561629, 0.31107238, 0.66410256]],\n",
              " \n",
              "        [[0.04672897, 0.36923901, 0.54132648, 0.89487179],\n",
              "         [0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "         [0.03738318, 0.36173633, 0.52408036, 0.77692308],\n",
              "         ...,\n",
              "         [0.05607477, 0.23901393, 0.31309054, 0.72820513],\n",
              "         [0.06542056, 0.22561629, 0.31107238, 0.66410256],\n",
              "         [0.06542056, 0.21221865, 0.30923768, 0.6       ]],\n",
              " \n",
              "        [[0.03738318, 0.36548767, 0.53050179, 0.83589744],\n",
              "         [0.03738318, 0.36173633, 0.52408036, 0.77692308],\n",
              "         [0.04672897, 0.35798499, 0.53141914, 0.71794872],\n",
              "         ...,\n",
              "         [0.06542056, 0.22561629, 0.31107238, 0.66410256],\n",
              "         [0.06542056, 0.21221865, 0.30923768, 0.6       ],\n",
              "         [0.13084112, 0.2170418 , 0.3055683 , 0.6       ]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[0.08411215, 0.34297964, 0.50389872, 0.6       ],\n",
              "         [0.09345794, 0.34083601, 0.49821117, 0.6       ],\n",
              "         [0.36448598, 0.34244373, 0.49380791, 0.6       ],\n",
              "         ...,\n",
              "         [0.08411215, 0.17470525, 0.32134667, 0.24615385],\n",
              "         [0.09345794, 0.17524116, 0.31749381, 0.26923077],\n",
              "         [0.09345794, 0.17577706, 0.31400789, 0.29230769]],\n",
              " \n",
              "        [[0.09345794, 0.34083601, 0.49821117, 0.6       ],\n",
              "         [0.36448598, 0.34244373, 0.49380791, 0.6       ],\n",
              "         [0.36448598, 0.34405145, 0.48830383, 0.6       ],\n",
              "         ...,\n",
              "         [0.09345794, 0.17524116, 0.31749381, 0.26923077],\n",
              "         [0.09345794, 0.17577706, 0.31400789, 0.29230769],\n",
              "         [0.1682243 , 0.17631297, 0.31208146, 0.31538462]],\n",
              " \n",
              "        [[0.36448598, 0.34244373, 0.49380791, 0.6       ],\n",
              "         [0.36448598, 0.34405145, 0.48830383, 0.6       ],\n",
              "         [0.35514019, 0.34565916, 0.48261627, 0.6       ],\n",
              "         ...,\n",
              "         [0.09345794, 0.17577706, 0.31400789, 0.29230769],\n",
              "         [0.1682243 , 0.17631297, 0.31208146, 0.31538462],\n",
              "         [0.41121495, 0.17684887, 0.3100633 , 0.33846154]]]),\n",
              " array([0.06542056, 0.13084112, 0.34579439, 0.26168224, 0.24299065,\n",
              "        0.38317757, 0.25233645, 0.27102804, 0.31775701, 0.24299065,\n",
              "        0.51401869, 1.        , 0.69158879, 0.35514019, 0.31775701,\n",
              "        0.30841121, 0.26168224, 0.20560748, 0.10280374, 0.09345794,\n",
              "        0.08411215, 0.09345794, 0.11214953, 0.12149533, 0.09345794,\n",
              "        0.08411215, 0.08411215, 0.09345794, 0.09345794, 0.1682243 ,\n",
              "        0.41121495, 0.18691589]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8AvmYYcNyOR"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.LSTM(128, input_shape= (win_length, num_features), return_sequences=True))\n",
        "model.add(tf.keras.layers.LeakyReLU(alpha=0.5)) \n",
        "model.add(tf.keras.layers.LSTM(128, return_sequences=True))\n",
        "model.add(tf.keras.layers.LeakyReLU(alpha=0.5)) \n",
        "model.add(tf.keras.layers.Dropout(0.3)) \n",
        "model.add(tf.keras.layers.LSTM(64, return_sequences=False))\n",
        "model.add(tf.keras.layers.Dropout(0.3)) \n",
        "model.add(tf.keras.layers.Dense(1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dQ-DlXiLswo",
        "outputId": "f23142e8-1a72-4614-d5ae-a246eda6cddf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 720, 128)          68096     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 720, 128)          0         \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 720, 128)          131584    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 720, 128)          0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 720, 128)          0         \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 64)                49408     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 249,153\n",
            "Trainable params: 249,153\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHnl-d8824y0",
        "outputId": "900e0c0e-c08c-4a35-bd47-c02cd8fcc357",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        }
      },
      "source": [
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                                                    patience=2,\n",
        "                                                    mode='min')\n",
        "\n",
        "model.compile(loss=tf.losses.MeanSquaredError(),\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=[tf.metrics.MeanAbsoluteError()])\n",
        "\n",
        "history = model.fit_generator(train_generator, epochs=50,\n",
        "                    validation_data=test_generator,\n",
        "                    shuffle=False,\n",
        "                    callbacks=[early_stopping])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-24-3b00c92e395b>:12: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/50\n",
            "471/471 [==============================] - 56s 118ms/step - loss: 0.0083 - mean_absolute_error: 0.0501 - val_loss: 0.0057 - val_mean_absolute_error: 0.0493\n",
            "Epoch 2/50\n",
            "471/471 [==============================] - 55s 116ms/step - loss: 0.0066 - mean_absolute_error: 0.0424 - val_loss: 0.0043 - val_mean_absolute_error: 0.0393\n",
            "Epoch 3/50\n",
            "471/471 [==============================] - 55s 116ms/step - loss: 0.0047 - mean_absolute_error: 0.0344 - val_loss: 0.0034 - val_mean_absolute_error: 0.0314\n",
            "Epoch 4/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0043 - mean_absolute_error: 0.0322 - val_loss: 0.0033 - val_mean_absolute_error: 0.0325\n",
            "Epoch 5/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0041 - mean_absolute_error: 0.0315 - val_loss: 0.0032 - val_mean_absolute_error: 0.0296\n",
            "Epoch 6/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0041 - mean_absolute_error: 0.0312 - val_loss: 0.0032 - val_mean_absolute_error: 0.0304\n",
            "Epoch 7/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0041 - mean_absolute_error: 0.0312 - val_loss: 0.0032 - val_mean_absolute_error: 0.0291\n",
            "Epoch 8/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0040 - mean_absolute_error: 0.0308 - val_loss: 0.0032 - val_mean_absolute_error: 0.0281\n",
            "Epoch 9/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0040 - mean_absolute_error: 0.0308 - val_loss: 0.0033 - val_mean_absolute_error: 0.0305\n",
            "Epoch 10/50\n",
            "471/471 [==============================] - 55s 117ms/step - loss: 0.0040 - mean_absolute_error: 0.0306 - val_loss: 0.0032 - val_mean_absolute_error: 0.0284\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4q1O2Hw5tXI",
        "outputId": "95a82019-7a0d-4699-b54a-fab02056cfa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "model.evaluate_generator(test_generator, verbose=0)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-25-ef1e1eabfd74>:1: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.003197350772097707, 0.028392525389790535]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wx6HG0ZjXodP",
        "outputId": "9f419171-917b-46c7-882d-880417704e32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "predictions=model.predict_generator(test_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-26-6ca49477046f>:1: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.predict, which supports generators.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEoR4K3pYDnR",
        "outputId": "665d5cde-f4db-48e3-ab3e-95c8274d4db6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "predictions.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3227"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
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      "cell_type": "code",
      "metadata": {
        "id": "GMC2ewo0YRDW",
        "outputId": "c26825d8-6128-4ab3-9d1d-e73404c0125d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.06838206],\n",
              "       [0.09586674],\n",
              "       [0.11981881],\n",
              "       ...,\n",
              "       [0.09129672],\n",
              "       [0.24887833],\n",
              "       [0.26383352]], dtype=float32)"
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        "outputId": "02a6ee4a-85b9-4f6b-d171-e1d5c0b3c529",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
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      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3364486 , 0.54205607, 0.28971963, ..., 0.24299065, 0.38317757,\n",
              "       0.39252336])"
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      "metadata": {
        "id": "tiw-WJ9f9OUV",
        "outputId": "8f593356-9083-40fa-8482-4657ea6090f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
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      "source": [
        "x_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.3364486 , 0.34297964, 0.30923768, 0.6       ],\n",
              "       [0.54205607, 0.34726688, 0.3055683 , 0.6       ],\n",
              "       [0.28971963, 0.35155413, 0.30455921, 0.6       ],\n",
              "       ...,\n",
              "       [0.24299065, 0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.38317757, 0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.39252336, 0.87459807, 0.53875791, 0.4       ]])"
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            "tags": []
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          "execution_count": 30
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        "outputId": "c02e0140-ebb9-4246-d6c6-87f483ddabb4",
        "colab": {
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          "height": 142
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      "source": [
        "x_test[:,1:][win_length:]"
      ],
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.45337621, 0.20851298, 0.6       ],\n",
              "       [0.47106109, 0.18282726, 0.6       ],\n",
              "       [0.48874598, 0.18466196, 0.6       ],\n",
              "       ...,\n",
              "       [0.88317256, 0.53866618, 0.37435897],\n",
              "       [0.87888532, 0.54949087, 0.38717949],\n",
              "       [0.87459807, 0.53875791, 0.4       ]])"
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            "tags": []
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          "execution_count": 31
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    {
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      "metadata": {
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      },
      "source": [
        "df_pred=pd.concat([pd.DataFrame(predictions), pd.DataFrame(x_test[:,1:][win_length:])],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "metadata": {
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        "outputId": "548060ab-9951-4f11-c997-9e4381650945",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        }
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      "source": [
        "df_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "             0         0         1         2\n",
              "0     0.068382  0.453376  0.208513  0.600000\n",
              "1     0.095867  0.471061  0.182827  0.600000\n",
              "2     0.119819  0.488746  0.184662  0.600000\n",
              "3     0.334295  0.506431  0.193996  0.600000\n",
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              "...        ...       ...       ...       ...\n",
              "3222  0.091904  0.891747  0.537657  0.348718\n",
              "3223  0.105139  0.887460  0.536006  0.361538\n",
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              "3226  0.263834  0.874598  0.538758  0.400000\n",
              "\n",
              "[3227 rows x 4 columns]"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "noykl7lh9hmT"
      },
      "source": [
        "rev_trans=scaler.inverse_transform(df_pred)"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "D2vcu4tBAf4n",
        "outputId": "4f9b26c7-9c94-4643-964d-f28b33dbfda1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
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      "source": [
        "rev_trans"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 83.16880636,   9.1       ,  34.6       ,  40.        ],\n",
              "       [112.57741153,   9.65      ,  33.66666667,  40.        ],\n",
              "       [138.20612311,  10.2       ,  33.73333333,  40.        ],\n",
              "       ...,\n",
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              "       [276.29981309,  22.33333333,  46.99      ,  26.16666667],\n",
              "       [292.30186939,  22.2       ,  46.6       ,  27.        ]])"
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    {
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      "metadata": {
        "id": "5eopbjZZBRUs"
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      "source": [
        "df_final=df_input[predictions.shape[0]*-1:]"
      ],
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "iEgc_FyMBjSO",
        "outputId": "572c5b45-d879-4967-adaf-399ad439074c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
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      "source": [
        "df_final.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Appliances    3227\n",
              "T_out         3227\n",
              "RH_1          3227\n",
              "Visibility    3227\n",
              "dtype: int64"
            ]
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            "tags": []
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          "execution_count": 37
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        "colab": {
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          "height": 144
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      "source": [
        "df_final['App_Pred']=rev_trans[:,0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
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      "metadata": {
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        "colab": {
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      "source": [
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      "execution_count": null,
      "outputs": [
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              "      <td>270</td>\n",
              "      <td>22.466667</td>\n",
              "      <td>46.596667</td>\n",
              "      <td>25.333333</td>\n",
              "      <td>107.687488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19733</th>\n",
              "      <td>420</td>\n",
              "      <td>22.333333</td>\n",
              "      <td>46.990000</td>\n",
              "      <td>26.166667</td>\n",
              "      <td>276.299813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19734</th>\n",
              "      <td>430</td>\n",
              "      <td>22.200000</td>\n",
              "      <td>46.600000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>292.301869</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3227 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Appliances      T_out       RH_1  Visibility    App_Pred\n",
              "16508          90   9.100000  34.600000   40.000000   83.168806\n",
              "16509         120   9.650000  33.666667   40.000000  112.577412\n",
              "16510         390  10.200000  33.733333   40.000000  138.206123\n",
              "16511         240  10.750000  34.072500   40.000000  367.695368\n",
              "16512         120  11.300000  33.630000   40.000000  172.002321\n",
              "...           ...        ...        ...         ...         ...\n",
              "19730         100  22.733333  46.560000   23.666667  108.337263\n",
              "19731          90  22.600000  46.500000   24.500000  122.498477\n",
              "19732         270  22.466667  46.596667   25.333333  107.687488\n",
              "19733         420  22.333333  46.990000   26.166667  276.299813\n",
              "19734         430  22.200000  46.600000   27.000000  292.301869\n",
              "\n",
              "[3227 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig7S76aZApyp",
        "outputId": "5bd338b3-d7b5-4b0f-bfcc-33cd3b97fa7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "source": [
        "df_final[['Appliances','App_Pred']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f646df9a278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "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": "grtp4xctGwDY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}