Multivariate_Time_Series_Modeling_via_LSTM.ipynb

{
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    "<a href=\"https://colab.research.google.com/github/ugururesin/Deep_Learning_Projects/blob/main/Multivariate_Time_Series_Modeling_via_LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "jDYCOMODo38L"
   },
   "outputs": [],
   "source": [
    "# IMPORTING LIBRARIRES\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "f9_JzFh0qL8w"
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (10,8)\n",
    "mpl.rcParams['axes.grid'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "YszmZQq6qdSz"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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     "height": 388
    },
    "id": "TM8uSLr1qqsS",
    "outputId": "beb5291c-e0a3-4483-9e06-0e44f04ab868"
   },
   "outputs": [
    {
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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>...</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",
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       "      <th>0</th>\n",
       "      <td>2016-01-11 17:00:00</td>\n",
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       "      <td>...</td>\n",
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       "      <td>45.53</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>733.5</td>\n",
       "      <td>92.0</td>\n",
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       "      <td>63.000000</td>\n",
       "      <td>5.3</td>\n",
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       "      <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>19.89</td>\n",
       "      <td>46.693333</td>\n",
       "      <td>19.2</td>\n",
       "      <td>44.722500</td>\n",
       "      <td>19.79</td>\n",
       "      <td>44.790000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>45.56</td>\n",
       "      <td>6.483333</td>\n",
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       "      <td>18.606195</td>\n",
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       "      <td>2016-01-11 17:20:00</td>\n",
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       "      <td>19.89</td>\n",
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       "      <td>19.2</td>\n",
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       "      <td>19.79</td>\n",
       "      <td>44.933333</td>\n",
       "      <td>18.926667</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>45.50</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>733.7</td>\n",
       "      <td>92.0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>55.333333</td>\n",
       "      <td>5.1</td>\n",
       "      <td>28.642668</td>\n",
       "      <td>28.642668</td>\n",
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       "      <td>2016-01-11 17:30:00</td>\n",
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       "      <td>45.40</td>\n",
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       "      <td>2016-01-11 17:40:00</td>\n",
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       "      <td>45.000000</td>\n",
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       "      <td>4.9</td>\n",
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       "<p>5 rows × 29 columns</p>\n",
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      "text/plain": [
       "                  date  Appliances  lights     T1       RH_1    T2       RH_2  \\\n",
       "0  2016-01-11 17:00:00          60      30  19.89  47.596667  19.2  44.790000   \n",
       "1  2016-01-11 17:10:00          60      30  19.89  46.693333  19.2  44.722500   \n",
       "2  2016-01-11 17:20:00          50      30  19.89  46.300000  19.2  44.626667   \n",
       "3  2016-01-11 17:30:00          50      40  19.89  46.066667  19.2  44.590000   \n",
       "4  2016-01-11 17:40:00          60      40  19.89  46.333333  19.2  44.530000   \n",
       "\n",
       "      T3       RH_3         T4  ...         T9   RH_9     T_out  Press_mm_hg  \\\n",
       "0  19.79  44.730000  19.000000  ...  17.033333  45.53  6.600000        733.5   \n",
       "1  19.79  44.790000  19.000000  ...  17.066667  45.56  6.483333        733.6   \n",
       "2  19.79  44.933333  18.926667  ...  17.000000  45.50  6.366667        733.7   \n",
       "3  19.79  45.000000  18.890000  ...  17.000000  45.40  6.250000        733.8   \n",
       "4  19.79  45.000000  18.890000  ...  17.000000  45.40  6.133333        733.9   \n",
       "\n",
       "   RH_out  Windspeed  Visibility  Tdewpoint        rv1        rv2  \n",
       "0    92.0   7.000000   63.000000        5.3  13.275433  13.275433  \n",
       "1    92.0   6.666667   59.166667        5.2  18.606195  18.606195  \n",
       "2    92.0   6.333333   55.333333        5.1  28.642668  28.642668  \n",
       "3    92.0   6.000000   51.500000        5.0  45.410389  45.410389  \n",
       "4    92.0   5.666667   47.666667        4.9  10.084097  10.084097  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1n9G0lxAvtfU"
   },
   "source": [
    "**Data Set Information:**\n",
    "\n",
    "The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters).\n",
    "\n",
    "For more information about the house, data collection, R scripts and figures, please refer to the paper and to the following github repository:\n",
    "\n",
    "[Web Link]\n",
    "\n",
    "\n",
    "**Attribute Information:**  \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",
    "Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data.\n",
    "\n",
    "\n",
    "**Relevant Papers:**  \n",
    "Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788,  \n",
    "[Web Link].\n",
    "\n",
    "\n",
    "**Citation Request:**  \n",
    "Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788,  \n",
    "[Web Link]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qOAjTtSXqsCb",
    "outputId": "df64f5c0-c93a-48c8-974e-017da9d5cbbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Fl8s8o80Mb0"
   },
   "source": [
    "### Data Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "HPOrJs3p0Jdm"
   },
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 563
    },
    "id": "9fa-Kfj153y9",
    "outputId": "1cd2d727-97bd-4891-aed0-cc2e888b5f95"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='date'>, <AxesSubplot:xlabel='date'>,\n",
       "       <AxesSubplot:xlabel='date'>, <AxesSubplot:xlabel='date'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('date')[['Appliances', 'T_out', 'RH_1', 'Visibility']].plot(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "xBQ8NuG65qlj"
   },
   "outputs": [],
   "source": [
    "df_input = df[['Appliances', 'T_out', 'RH_1', 'Visibility']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "Ox9R-3ru5wI0",
    "outputId": "3ec20c64-6725-46e4-8d17-e2324df1d44f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "VzMEWdnC5wr8",
    "outputId": "5bba0f13-735f-441d-bcd1-c2e800985236"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "        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"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_input.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "id": "3soNz0qA8TVx",
    "outputId": "e97632ee-75bf-4999-a273-4d6edc192eb1"
   },
   "outputs": [
    {
     "data": {
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       "      <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]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_input.query(\"Appliances>500\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lzCdk6ZWJ2IP"
   },
   "source": [
    "The reason of scaling the data before feeding into the neural network model, it provides faster convergence!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "cJv5w3nR_FFa"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "data_scaled = scaler.fit_transform(df_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "X4yE59KYKMtw",
    "outputId": "95bda1d1-bd0b-491e-b698-fad151887a5a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</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>0.081958</td>\n",
       "      <td>0.399089</td>\n",
       "      <td>0.364271</td>\n",
       "      <td>0.574321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.095818</td>\n",
       "      <td>0.170978</td>\n",
       "      <td>0.109512</td>\n",
       "      <td>0.181457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.037383</td>\n",
       "      <td>0.278671</td>\n",
       "      <td>0.283735</td>\n",
       "      <td>0.430769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.046729</td>\n",
       "      <td>0.383173</td>\n",
       "      <td>0.347675</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.084112</td>\n",
       "      <td>0.495445</td>\n",
       "      <td>0.441519</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0             1             2             3\n",
       "count  19735.000000  19735.000000  19735.000000  19735.000000\n",
       "mean       0.081958      0.399089      0.364271      0.574321\n",
       "std        0.095818      0.170978      0.109512      0.181457\n",
       "min        0.000000      0.000000      0.000000      0.000000\n",
       "25%        0.037383      0.278671      0.283735      0.430769\n",
       "50%        0.046729      0.383173      0.347675      0.600000\n",
       "75%        0.084112      0.495445      0.441519      0.600000\n",
       "max        1.000000      1.000000      1.000000      1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data_scaled).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "7FT3dwI-KP0i"
   },
   "outputs": [],
   "source": [
    "features = data_scaled\n",
    "target = data_scaled[:,0] #\"Appliances\" will be the target!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yMx065zVMimX",
    "outputId": "77df3a39-2c69-41ed-d94d-703e9783a15b"
   },
   "outputs": [
    {
     "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]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TimeseriesGenerator(features, target, length=2, sampling_rate=1, batch_size=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "rw-TaViAMqQF"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=123, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tR8EbvGUMyC9",
    "outputId": "5127338d-e3cf-47ef-9077-0856fcdb9330"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape:  (15788, 4)\n",
      "Test shape:  (3947, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train shape: \", x_train.shape)\n",
    "print(\"Test shape: \", x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "-ruHjbwAM2KS"
   },
   "outputs": [],
   "source": [
    "# TRAINING/TEST GENERATORS\n",
    "\n",
    "'''\n",
    "The data is sampled in every 10 minutes which means 6 datapoints per hour.\n",
    "That is 144 datapoints per day and 720 datapoints for 5 days.\n",
    "'''\n",
    "win_length = 720\n",
    "batch_size = 32\n",
    "num_features = 4\n",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0njDqzf_Qan8",
    "outputId": "bff2796e-2564-47a9-8f2e-178fb01b2cdb"
   },
   "outputs": [
    {
     "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]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "32 observations\n",
    "Each observation's length is 720\n",
    "Each observation contain 4 features\n",
    "'''\n",
    "train_generator[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "rr9QBdyWQ_VG"
   },
   "outputs": [],
   "source": [
    "# CREATING THE MODEL\n",
    "model = tf.keras.Sequential()\n",
    "\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gUvILdLjS0PC",
    "outputId": "616a51c9-66b7-439d-88de-734ae04da04b"
   },
   "outputs": [
    {
     "name": "stdout",
     "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"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oS5PbQ3nS7NK",
    "outputId": "2c3fcbe8-26ac-48bf-f301-a45c36727fd5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "471/471 [==============================] - 120s 249ms/step - loss: 0.0058 - mean_absolute_error: 0.0395 - val_loss: 0.0036 - val_mean_absolute_error: 0.0326\n",
      "Epoch 2/50\n",
      "471/471 [==============================] - 214s 454ms/step - loss: 0.0043 - mean_absolute_error: 0.0329 - val_loss: 0.0033 - val_mean_absolute_error: 0.0288\n",
      "Epoch 3/50\n",
      "471/471 [==============================] - 213s 453ms/step - loss: 0.0041 - mean_absolute_error: 0.0315 - val_loss: 0.0032 - val_mean_absolute_error: 0.0281\n",
      "Epoch 4/50\n",
      "471/471 [==============================] - 214s 455ms/step - loss: 0.0040 - mean_absolute_error: 0.0313 - val_loss: 0.0032 - val_mean_absolute_error: 0.0282\n",
      "Epoch 5/50\n",
      "471/471 [==============================] - 213s 453ms/step - loss: 0.0040 - mean_absolute_error: 0.0310 - val_loss: 0.0032 - val_mean_absolute_error: 0.0278\n",
      "Epoch 6/50\n",
      "471/471 [==============================] - 213s 453ms/step - loss: 0.0040 - mean_absolute_error: 0.0306 - val_loss: 0.0032 - val_mean_absolute_error: 0.0276\n",
      "Epoch 7/50\n",
      "471/471 [==============================] - 61s 130ms/step - loss: 0.0040 - mean_absolute_error: 0.0308 - val_loss: 0.0032 - val_mean_absolute_error: 0.0269\n",
      "Epoch 8/50\n",
      "471/471 [==============================] - 44s 94ms/step - loss: 0.0040 - mean_absolute_error: 0.0306 - val_loss: 0.0032 - val_mean_absolute_error: 0.0295\n",
      "Epoch 9/50\n",
      "471/471 [==============================] - 45s 95ms/step - loss: 0.0039 - mean_absolute_error: 0.0305 - val_loss: 0.0032 - val_mean_absolute_error: 0.0284\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "If the validation loss is not improving in \"2\" iterations (patience),\n",
    "the training stops early!\n",
    "'''\n",
    "\n",
    "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "33x1x85sTduF",
    "outputId": "86ba4374-2e49-4d7b-9ff6-08936aea0948"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smws7105/anaconda3/lib/python3.8/site-packages/keras/engine/training.py:2006: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  warnings.warn('`Model.evaluate_generator` is deprecated and '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.003199260449036956, 0.02841751091182232]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate_generator(test_generator, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z0Lyl03NUA02",
    "outputId": "4afb25ea-6002-4982-f169-6bae4f382f1a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smws7105/anaconda3/lib/python3.8/site-packages/keras/engine/training.py:2035: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict_generator(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X6rgywb0UGih",
    "outputId": "95b7d3a5-a314-47b1-e67b-26321ac2baa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of predictions:  3227\n",
      "The value of first prediction:  [0.07044706]\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of predictions: \", predictions.shape[0])\n",
    "print(\"The value of first prediction: \", predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4lT6bjugUIMX",
    "outputId": "65624c60-3d5e-4642-e4d5-1ba52e72063b"
   },
   "outputs": [
    {
     "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       ]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test[:,1:][win_length:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "vtSdHNMgULj1"
   },
   "outputs": [],
   "source": [
    "df_pred = pd.concat([pd.DataFrame(predictions), pd.DataFrame(x_test[:,1:][win_length:])], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "25YJceWOiN3P"
   },
   "outputs": [],
   "source": [
    "rev_trans = scaler.inverse_transform(df_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "csdRzjHTiR0d",
    "outputId": "0a5e1a7f-afbb-4e7e-862d-4b4539ea6aa6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 85.37835151,   9.1       ,  34.6       ,  40.        ],\n",
       "       [117.10351162,   9.65      ,  33.66666667,  40.        ],\n",
       "       [141.7304863 ,  10.2       ,  33.73333333,  40.        ],\n",
       "       ...,\n",
       "       [ 99.54554439,  22.46666667,  46.59666667,  25.33333333],\n",
       "       [294.49117333,  22.33333333,  46.99      ,  26.16666667],\n",
       "       [291.4433676 ,  22.2       ,  46.6       ,  27.        ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "5BbFdtv9iSSe"
   },
   "outputs": [],
   "source": [
    "df_final = df_input[predictions.shape[0]*-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KfPCx4XyieGr",
    "outputId": "5c747b4b-a439-4b02-c8bc-0d7b9fcf1beb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Appliances    3227\n",
       "T_out         3227\n",
       "RH_1          3227\n",
       "Visibility    3227\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 521
    },
    "id": "ZaXNcEVeigw0",
    "outputId": "7ee7d0e3-af20-4b4e-822a-bbbda578b38a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-33b61a21d4ec>: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",
      "  df_final['App_Pred'] = rev_trans[:,0]\n"
     ]
    },
    {
     "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",
       "      <th>App_Pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16508</th>\n",
       "      <td>90</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>34.600000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>85.378352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16509</th>\n",
       "      <td>120</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>33.666667</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>117.103512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16510</th>\n",
       "      <td>390</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>33.733333</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>141.730486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16511</th>\n",
       "      <td>240</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>34.072500</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>383.177227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16512</th>\n",
       "      <td>120</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>33.630000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>165.471320</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>100</td>\n",
       "      <td>22.733333</td>\n",
       "      <td>46.560000</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>99.541718</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",
       "      <td>115.991600</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",
       "      <td>99.545544</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>294.491173</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>291.443368</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   85.378352\n",
       "16509         120   9.650000  33.666667   40.000000  117.103512\n",
       "16510         390  10.200000  33.733333   40.000000  141.730486\n",
       "16511         240  10.750000  34.072500   40.000000  383.177227\n",
       "16512         120  11.300000  33.630000   40.000000  165.471320\n",
       "...           ...        ...        ...         ...         ...\n",
       "19730         100  22.733333  46.560000   23.666667   99.541718\n",
       "19731          90  22.600000  46.500000   24.500000  115.991600\n",
       "19732         270  22.466667  46.596667   25.333333   99.545544\n",
       "19733         420  22.333333  46.990000   26.166667  294.491173\n",
       "19734         430  22.200000  46.600000   27.000000  291.443368\n",
       "\n",
       "[3227 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['App_Pred'] = rev_trans[:,0]\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 501
    },
    "id": "IkHjIXpFiqDm",
    "outputId": "79aff942-8e4e-489d-f465-1dec606af5a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_final[['Appliances', 'App_Pred']].plot()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNDTFFCynuUStZWUHEbNh12",
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Multivariate Time Series Modeling via LSTM.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}