Univariate Time Series Forecasting - LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "clim = pd.read_csv('DataRepository/jena_climate_2009_2016.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date Time</th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2009 00:10:00</td>\n",
       "      <td>996.52</td>\n",
       "      <td>-8.02</td>\n",
       "      <td>265.40</td>\n",
       "      <td>-8.90</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.94</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1307.75</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.75</td>\n",
       "      <td>152.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2009 00:20:00</td>\n",
       "      <td>996.57</td>\n",
       "      <td>-8.41</td>\n",
       "      <td>265.01</td>\n",
       "      <td>-9.28</td>\n",
       "      <td>93.4</td>\n",
       "      <td>3.23</td>\n",
       "      <td>3.02</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.03</td>\n",
       "      <td>1309.80</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.50</td>\n",
       "      <td>136.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2009 00:30:00</td>\n",
       "      <td>996.53</td>\n",
       "      <td>-8.51</td>\n",
       "      <td>264.91</td>\n",
       "      <td>-9.31</td>\n",
       "      <td>93.9</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.88</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1310.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.63</td>\n",
       "      <td>171.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2009 00:40:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.31</td>\n",
       "      <td>265.12</td>\n",
       "      <td>-9.07</td>\n",
       "      <td>94.2</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1309.19</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>198.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2009 00:50:00</td>\n",
       "      <td>996.51</td>\n",
       "      <td>-8.27</td>\n",
       "      <td>265.15</td>\n",
       "      <td>-9.04</td>\n",
       "      <td>94.1</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1309.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.63</td>\n",
       "      <td>214.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  \\\n",
       "0  01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90    93.3   \n",
       "1  01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28    93.4   \n",
       "2  01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31    93.9   \n",
       "3  01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07    94.2   \n",
       "4  01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04    94.1   \n",
       "\n",
       "   VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  \\\n",
       "0          3.33          3.11          0.22       1.94             3.12   \n",
       "1          3.23          3.02          0.21       1.89             3.03   \n",
       "2          3.21          3.01          0.20       1.88             3.02   \n",
       "3          3.26          3.07          0.19       1.92             3.08   \n",
       "4          3.27          3.08          0.19       1.92             3.09   \n",
       "\n",
       "   rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "0       1307.75      1.03           1.75     152.3  \n",
       "1       1309.80      0.72           1.50     136.1  \n",
       "2       1310.24      0.19           0.63     171.6  \n",
       "3       1309.19      0.34           0.50     198.0  \n",
       "4       1309.00      0.32           0.63     214.3  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clim = clim.reset_index()\n",
    "clim.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01.01.2009 00:10:00</td>\n",
       "      <td>-8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01.01.2009 00:20:00</td>\n",
       "      <td>-8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01.01.2009 00:30:00</td>\n",
       "      <td>-8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01.01.2009 00:40:00</td>\n",
       "      <td>-8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01.01.2009 00:50:00</td>\n",
       "      <td>-8.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime     T\n",
       "0  01.01.2009 00:10:00 -8.02\n",
       "1  01.01.2009 00:20:00 -8.41\n",
       "2  01.01.2009 00:30:00 -8.51\n",
       "3  01.01.2009 00:40:00 -8.31\n",
       "4  01.01.2009 00:50:00 -8.27"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = clim[['Date Time','T (degC)']].rename(columns = {'T (degC)':'T','Date Time':'datetime'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(figsize = (15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['datetime'] = pd.to_datetime(df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['datetime'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "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>datetime</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01 00:10:00</td>\n",
       "      <td>-8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01 00:20:00</td>\n",
       "      <td>-8.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-01 00:30:00</td>\n",
       "      <td>-8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-01 00:40:00</td>\n",
       "      <td>-8.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-01 00:50:00</td>\n",
       "      <td>-8.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2009-01-01 01:00:00</td>\n",
       "      <td>-8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009-01-01 01:10:00</td>\n",
       "      <td>-7.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009-01-01 01:20:00</td>\n",
       "      <td>-7.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009-01-01 01:30:00</td>\n",
       "      <td>-7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009-01-01 01:40:00</td>\n",
       "      <td>-8.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime     T\n",
       "0 2009-01-01 00:10:00 -8.02\n",
       "1 2009-01-01 00:20:00 -8.41\n",
       "2 2009-01-01 00:30:00 -8.51\n",
       "3 2009-01-01 00:40:00 -8.31\n",
       "4 2009-01-01 00:50:00 -8.27\n",
       "5 2009-01-01 01:00:00 -8.05\n",
       "6 2009-01-01 01:10:00 -7.62\n",
       "7 2009-01-01 01:20:00 -7.62\n",
       "8 2009-01-01 01:30:00 -7.91\n",
       "9 2009-01-01 01:40:00 -8.43"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking each 6th record as we need hourly data, so we can ignore every other record (which are on 10 min level)\n",
    "\n",
    "# We can also take mean/median of each consecutive 6 records if that is a business requirement, but right now it is not.\n",
    "\n",
    "df_hour_lvl = df[5::6].reset_index().drop('index', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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>datetime</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01 01:00:00</td>\n",
       "      <td>-8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01 02:00:00</td>\n",
       "      <td>-8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-01 03:00:00</td>\n",
       "      <td>-8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-01 04:00:00</td>\n",
       "      <td>-9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-01 05:00:00</td>\n",
       "      <td>-9.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime     T\n",
       "0 2009-01-01 01:00:00 -8.05\n",
       "1 2009-01-01 02:00:00 -8.88\n",
       "2 2009-01-01 03:00:00 -8.81\n",
       "3 2009-01-01 04:00:00 -9.05\n",
       "4 2009-01-01 05:00:00 -9.63"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour_lvl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the data distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='T', ylabel='Density'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14,5))\n",
    "sns.distplot(df_hour_lvl['T'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The data is a very good normally distributed data. If our data was not normally distributed, then we had to convert it into normal data using outlier removal or data transformation (usually Box-Cox Transformation) or both. In my experience, LSTMs provide very bad forecast on skewed data. As of now, this data is fine and we can proceed with this data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train - Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we cannot simply provide a sequence of data points to an LSTM model for training and testing, \n",
    "# the input X sequence must be a 2D array [no of records, n_input], while the input y sequence must be a 1D array\n",
    "\n",
    "def Sequential_Input_LSTM(df, input_sequence):\n",
    "    df_np = df.to_numpy()\n",
    "    X = []\n",
    "    y = []\n",
    "    \n",
    "    for i in range(len(df_np) - input_sequence):\n",
    "        row = [a for a in df_np[i:i + input_sequence]]\n",
    "        X.append(row)\n",
    "        label = df_np[i + input_sequence]\n",
    "        y.append(label)\n",
    "        \n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_input = 10      # number of historical inputs to be used for forecasting the future time series \n",
    "\n",
    "df_min_model_data = df_hour_lvl['T']\n",
    "\n",
    "X, y = Sequential_Input_LSTM(df_min_model_data, n_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70081, 10), (70081,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training data\n",
    "X_train, y_train = X[:60000], y[:60000]\n",
    "\n",
    "# Validation data\n",
    "X_val, y_val = X[60000:65000], y[60000:65000]\n",
    "\n",
    "# Test data\n",
    "X_test, y_test = X[65000:], y[65000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 10), (60000,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the dimensions\n",
    "\n",
    "X_train.ndim, y_train.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5000, 10), (5000,))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape, y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5081, 10), (5081,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp :\n",
      "\n",
      "Total days      : 2920.4583333333335\n",
      "Training days   : 2500.0\n",
      "Validation days : 208.33333333333334\n",
      "Testing days    : 211.70833333333334\n"
     ]
    }
   ],
   "source": [
    "# days\n",
    "\n",
    "print('Temp :')\n",
    "print()\n",
    "print(f'Total days      : {len(df_hour_lvl)/24}')\n",
    "print(f'Training days   : {len(X_train)/24}')\n",
    "print(f'Validation days : {len(X_val)/24}')\n",
    "print(f'Testing days    : {len(X_test)/24}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential, save_model, load_model\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError\n",
    "from tensorflow.keras.optimizers import Adam "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  - We are using 3 layers of LSTM (this is called Stacked/Deep LSTM) instead of just 1 layer (called Simple LSTM), for better forecast. \n",
    "\n",
    "#### - Use \"return_sequences = True\" if using multiple LSTM layers, except in the last layer (otherwise we will face dimension mismatch error)\n",
    "\n",
    "#### - No of neurons in the last Dense layer is the shape we want in our output. In case of time series, we need 1D output. Hence, no of neurons in last Dense layer should be 1.\n",
    "\n",
    "#### - Using EarlyStopping() saves us a lot of time, it stops the model training once it realizes that there will be no more decrease in loss in further epochs and training can now be stopped earlier than described epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 10, 100)           40800     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 10, 100)           80400     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 50)                30200     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 8)                 408       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 151,817\n",
      "Trainable params: 151,817\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "n_features = 1                        # number of input variables used for forecast (here, only 1 i.e. temperature)\n",
    "\n",
    "model1 = Sequential()\n",
    "\n",
    "model1.add(InputLayer((n_input,n_features)))\n",
    "model1.add(LSTM(100, return_sequences = True))     \n",
    "model1.add(LSTM(100, return_sequences = True))\n",
    "model1.add(LSTM(50))\n",
    "model1.add(Dense(8, activation = 'relu'))\n",
    "model1.add(Dense(1, activation = 'linear'))\n",
    "\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop = EarlyStopping(monitor = 'val_loss', patience = 2)\n",
    "\n",
    "model1.compile(loss = MeanSquaredError(), optimizer = Adam(learning_rate = 0.0001), metrics = RootMeanSquaredError())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1875/1875 [==============================] - 40s 17ms/step - loss: 33.9425 - root_mean_squared_error: 5.8260 - val_loss: 1.7583 - val_root_mean_squared_error: 1.3260\n",
      "Epoch 2/50\n",
      "1875/1875 [==============================] - 31s 16ms/step - loss: 3.5450 - root_mean_squared_error: 1.8828 - val_loss: 0.5933 - val_root_mean_squared_error: 0.7703\n",
      "Epoch 3/50\n",
      "1875/1875 [==============================] - 31s 17ms/step - loss: 1.1408 - root_mean_squared_error: 1.0681 - val_loss: 0.5014 - val_root_mean_squared_error: 0.7081\n",
      "Epoch 4/50\n",
      "1875/1875 [==============================] - 31s 17ms/step - loss: 0.7663 - root_mean_squared_error: 0.8754 - val_loss: 0.4806 - val_root_mean_squared_error: 0.6933\n",
      "Epoch 5/50\n",
      "1875/1875 [==============================] - 30s 16ms/step - loss: 0.6773 - root_mean_squared_error: 0.8230 - val_loss: 0.4801 - val_root_mean_squared_error: 0.6929\n",
      "Epoch 6/50\n",
      "1875/1875 [==============================] - 32s 17ms/step - loss: 0.6472 - root_mean_squared_error: 0.8045 - val_loss: 0.4748 - val_root_mean_squared_error: 0.6891\n",
      "Epoch 7/50\n",
      "1875/1875 [==============================] - 32s 17ms/step - loss: 0.6355 - root_mean_squared_error: 0.7972 - val_loss: 0.5190 - val_root_mean_squared_error: 0.7204\n",
      "Epoch 8/50\n",
      "1875/1875 [==============================] - 30s 16ms/step - loss: 0.6315 - root_mean_squared_error: 0.7947 - val_loss: 0.4815 - val_root_mean_squared_error: 0.6939\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x21c4be8dd60>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(X_train, y_train, validation_data = (X_val, y_val), epochs = 50, callbacks = [early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "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>loss</th>\n",
       "      <th>root_mean_squared_error</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_root_mean_squared_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.942486</td>\n",
       "      <td>5.826018</td>\n",
       "      <td>1.758304</td>\n",
       "      <td>1.326011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.544997</td>\n",
       "      <td>1.882816</td>\n",
       "      <td>0.593335</td>\n",
       "      <td>0.770283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.140796</td>\n",
       "      <td>1.068081</td>\n",
       "      <td>0.501406</td>\n",
       "      <td>0.708100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.766337</td>\n",
       "      <td>0.875407</td>\n",
       "      <td>0.480619</td>\n",
       "      <td>0.693267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.677321</td>\n",
       "      <td>0.822995</td>\n",
       "      <td>0.480068</td>\n",
       "      <td>0.692869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.647160</td>\n",
       "      <td>0.804462</td>\n",
       "      <td>0.474805</td>\n",
       "      <td>0.689061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.635453</td>\n",
       "      <td>0.797153</td>\n",
       "      <td>0.519021</td>\n",
       "      <td>0.720431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.631482</td>\n",
       "      <td>0.794659</td>\n",
       "      <td>0.481487</td>\n",
       "      <td>0.693892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss  root_mean_squared_error  val_loss  val_root_mean_squared_error\n",
       "0  33.942486                 5.826018  1.758304                     1.326011\n",
       "1   3.544997                 1.882816  0.593335                     0.770283\n",
       "2   1.140796                 1.068081  0.501406                     0.708100\n",
       "3   0.766337                 0.875407  0.480619                     0.693267\n",
       "4   0.677321                 0.822995  0.480068                     0.692869\n",
       "5   0.647160                 0.804462  0.474805                     0.689061\n",
       "6   0.635453                 0.797153  0.519021                     0.720431\n",
       "7   0.631482                 0.794659  0.481487                     0.693892"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "losses_df1 = pd.DataFrame(model1.history.history)\n",
    "losses_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses_df1.plot(figsize = (10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 55.69%\n",
      "root_mean_squared_error: 74.62%\n"
     ]
    }
   ],
   "source": [
    "scores = model1.evaluate(X_test, y_test, verbose=0)\n",
    "print(\"%s: %.2f%%\" % (model1.metrics_names[0], scores[0]*100))\n",
    "print(\"%s: %.2f%%\" % (model1.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the Keras model if it's the final version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_model(model1, \"LSTM_Models/lstm_univariate.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load the Keras model afterwards, so that we don't have to train the model each time we restart our jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = load_model('LSTM/lstm_univariate.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict on Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12.233923 , 11.485084 , 11.884583 , ..., -1.7929276, -2.9912076,\n",
       "       -2.9950013], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions1 = model1.predict(X_test).flatten()\n",
    "test_predictions1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5081, 5081)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test), len(test_predictions1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the dimension of the output (should be 1 for time series forecast)\n",
    "\n",
    "test_predictions1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5081"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_list = []\n",
    "for i in range(len(X_test)):\n",
    "    X_test_list.append(X_test[i][0])\n",
    "    \n",
    "len(X_test_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5081"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions_df1 = pd.DataFrame({'X_test':list(X_test_list), 'LSTM Prediction':list(test_predictions1)})\n",
    "len(test_predictions_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "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>X_test</th>\n",
       "      <th>LSTM Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.27</td>\n",
       "      <td>12.233923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.85</td>\n",
       "      <td>11.485084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.65</td>\n",
       "      <td>11.884583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.85</td>\n",
       "      <td>16.209522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.09</td>\n",
       "      <td>20.267723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X_test  LSTM Prediction\n",
       "0   18.27        12.233923\n",
       "1   17.85        11.485084\n",
       "2   16.65        11.884583\n",
       "3   15.85        16.209522\n",
       "4   15.09        20.267723"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions_df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM temperature forecast on complete Test Data (211.7 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions_df1.plot(figsize = (15,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM temperature forecast on last 1 Month in the Test Data (720 hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions_df1[(len(X_test) - 720):].plot(figsize = (15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If the model is not providing good forecast, we need to try different things such as data scaling, data transformation, use more LSTM or Dense layers (requires more computing power) or hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now Making Future Forecast using LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def futureForecast(df, col, n_input, n_features, forecast_timeperiod, model):\n",
    "\n",
    "    x_input = np.array(df[len(df)-n_input:][col])\n",
    "\n",
    "    temp_input=list(x_input)\n",
    "\n",
    "    lst_output=[]\n",
    "    i=0\n",
    "\n",
    "    while(i < forecast_timeperiod):\n",
    "\n",
    "        if(len(temp_input) > n_input):\n",
    "\n",
    "            x_input = np.array(temp_input[1:])\n",
    "            x_input = x_input.reshape((1, n_input, n_features))\n",
    "            yhat = model.predict(x_input, verbose=0)\n",
    "            temp_input.append(yhat[0][0])\n",
    "            temp_input = temp_input[1:]\n",
    "            lst_output.append(yhat[0][0])\n",
    "\n",
    "            i=i+1\n",
    "\n",
    "        else:\n",
    "            x_input = x_input.reshape((1, n_input, n_features))\n",
    "            yhat = model.predict(x_input, verbose=0)\n",
    "            #print(yhat[0])\n",
    "            temp_input.append(yhat[0][0])\n",
    "            lst_output.append(yhat[0][0])\n",
    "\n",
    "            i=i+1\n",
    "            \n",
    "    return lst_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "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>datetime</th>\n",
       "      <th>T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-01-01 01:00:00</td>\n",
       "      <td>-8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-01-01 02:00:00</td>\n",
       "      <td>-8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-01-01 03:00:00</td>\n",
       "      <td>-8.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-01-01 04:00:00</td>\n",
       "      <td>-9.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-01-01 05:00:00</td>\n",
       "      <td>-9.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime     T\n",
       "0 2009-01-01 01:00:00 -8.05\n",
       "1 2009-01-01 02:00:00 -8.88\n",
       "2 2009-01-01 03:00:00 -8.81\n",
       "3 2009-01-01 04:00:00 -9.05\n",
       "4 2009-01-01 05:00:00 -9.63"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour_lvl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_input = 10\n",
    "n_features = 1\n",
    "forecast_timeperiod = 240         # next 10 days\n",
    "model = model1\n",
    "\n",
    "forecast_output = futureForecast(df_hour_lvl, 'T', n_input, n_features, forecast_timeperiod, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(forecast_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_10_days = df_hour_lvl['T'][len(df_hour_lvl) - 240:].tolist()\n",
    "\n",
    "next_10_days = pd.DataFrame(forecast_output, columns = ['FutureForecast'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "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>FutureForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.120708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.142042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.020559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.643923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-3.015779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FutureForecast\n",
       "0       -4.120708\n",
       "1       -4.142042\n",
       "2       -4.020559\n",
       "3       -3.643923\n",
       "4       -3.015779"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next_10_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,5))\n",
    "\n",
    "hist_axis = len(last_10_days)\n",
    "forecast_axis = hist_axis + len(next_10_days)\n",
    "\n",
    "plt.plot(np.arange(0,hist_axis),last_10_days, color = 'blue')\n",
    "plt.plot(np.arange(hist_axis,forecast_axis),next_10_days['FutureForecast'].tolist(), color = 'orange')\n",
    "\n",
    "plt.title('LSTM Forecast for Next 10 Days')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Temperature')\n",
    "\n",
    "# save the figure\n",
    "plt.savefig('Pics_Models/lstm_univariate_forecast_pic.png')\n",
    "plt.savefig('Pics_Models/lstm_univariate_forecast_pdf.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thank you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feel free to connect with me for further discussions on Time Series Analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LinkedIn : https://www.linkedin.com/in/vineet-pandya/\n",
    "\n",
    "#### Medium : https://medium.com/@vineet.pandya\n",
    "\n",
    "#### Email : vineet.pandya@yahoo.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}