Forecasting Using RNNs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "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>S4248SM144NCEN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>3459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>4002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>4564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>4221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            S4248SM144NCEN\n",
       "DATE                      \n",
       "1992-01-01            3459\n",
       "1992-02-01            3458\n",
       "1992-03-01            4002\n",
       "1992-04-01            4564\n",
       "1992-05-01            4221"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading data\n",
    "data = pd.read_csv('data/Alcohol_Sales.csv', index_col= 'DATE', parse_dates= True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1992-01-01', '1992-02-01', '1992-03-01', '1992-04-01',\n",
       "               '1992-05-01', '1992-06-01', '1992-07-01', '1992-08-01',\n",
       "               '1992-09-01', '1992-10-01',\n",
       "               ...\n",
       "               '2018-04-01', '2018-05-01', '2018-06-01', '2018-07-01',\n",
       "               '2018-08-01', '2018-09-01', '2018-10-01', '2018-11-01',\n",
       "               '2018-12-01', '2019-01-01'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=325, freq=None)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1992-01-01', '1992-02-01', '1992-03-01', '1992-04-01',\n",
       "               '1992-05-01', '1992-06-01', '1992-07-01', '1992-08-01',\n",
       "               '1992-09-01', '1992-10-01',\n",
       "               ...\n",
       "               '2018-04-01', '2018-05-01', '2018-06-01', '2018-07-01',\n",
       "               '2018-08-01', '2018-09-01', '2018-10-01', '2018-11-01',\n",
       "               '2018-12-01', '2019-01-01'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=325, freq='MS')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.freq = 'MS'\n",
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S4248SM144NCEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>325.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7886.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2914.269061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3031.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5231.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7481.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9977.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15504.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       S4248SM144NCEN\n",
       "count      325.000000\n",
       "mean      7886.400000\n",
       "std       2914.269061\n",
       "min       3031.000000\n",
       "25%       5231.000000\n",
       "50%       7481.000000\n",
       "75%       9977.000000\n",
       "max      15504.000000"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>3459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-01</th>\n",
       "      <td>3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-01</th>\n",
       "      <td>4002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-04-01</th>\n",
       "      <td>4564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-05-01</th>\n",
       "      <td>4221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales\n",
       "DATE             \n",
       "1992-01-01   3459\n",
       "1992-02-01   3458\n",
       "1992-03-01   4002\n",
       "1992-04-01   4564\n",
       "1992-05-01   4221"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(columns={'S4248SM144NCEN': 'sales'})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize = (15,5), legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "seasonal_decompose(data['sales'],model= 'mul').plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seasonal componenet\n",
    "seasonal_decompose(data['sales'],model= 'mul').seasonal.plot(figsize = (15,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trend component\n",
    "seasonal_decompose(data['sales'],model= 'mul').trend.plot(figsize = (15,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "325\n",
      "313\n"
     ]
    }
   ],
   "source": [
    "# train and test split\n",
    "# if you see the data it is monthly data so I would like to forecast 1 year (12) months into the future\n",
    "print(len(data))\n",
    "print(len(data)- 12) # subtracting 12 means I will forecast that data\n",
    "\n",
    "train = data.iloc[ :314]\n",
    "test = data.iloc[314: ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "## scaling the data (must for Deep Learning models\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "scaled_train = sc.fit_transform(train)\n",
    "scaled_test = sc.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model\n",
    "import keras\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_input = 2\n",
    "n_features = 1\n",
    "generator = TimeseriesGenerator(data = scaled_train, targets= scaled_train, length= n_input, batch_size= 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(314, 1)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "312"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.53481163],\n",
       "       [-1.53517398],\n",
       "       [-1.33805754],\n",
       "       [-1.13441887],\n",
       "       [-1.25870368]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what actually generator is giving ?\n",
    "scaled_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[-1.53481163]\n",
      "  [-1.53517398]]] \n",
      "\n",
      "[[-1.33805754]]\n"
     ]
    }
   ],
   "source": [
    "x, y = generator[0]\n",
    "print(x,'\\n')\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Note**: it is clear that it takes first 2 elements and giving output of 1 element which we have in training data, depending on requirement we can alter the values of **n_inputs** and **batch_size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_input = 12\n",
    "n_features = 1\n",
    "train_generator = TimeseriesGenerator(data = scaled_train, targets= scaled_train, length= n_input, batch_size= 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "n_input = 12\n",
    "n_features = 1\n",
    "model.add(LSTM(units = 150, activation = 'relu', input_shape = (n_input, n_features)))\n",
    "model.add(Dense(units = 1))\n",
    "model.compile(optimizer = 'adam', loss = 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 150)               91200     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 151       \n",
      "=================================================================\n",
      "Total params: 91,351\n",
      "Trainable params: 91,351\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.5624\n",
      "Epoch 2/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.1305\n",
      "Epoch 3/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.1212\n",
      "Epoch 4/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.1078\n",
      "Epoch 5/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0809\n",
      "Epoch 6/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0672\n",
      "Epoch 7/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0527\n",
      "Epoch 8/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.0461\n",
      "Epoch 9/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0356\n",
      "Epoch 10/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.0361\n",
      "Epoch 11/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.0308\n",
      "Epoch 12/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0328\n",
      "Epoch 13/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0346\n",
      "Epoch 14/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.0279\n",
      "Epoch 15/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0357\n",
      "Epoch 16/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0283\n",
      "Epoch 17/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0316\n",
      "Epoch 18/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0328\n",
      "Epoch 19/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.0288\n",
      "Epoch 20/25\n",
      "302/302 [==============================] - 1s 5ms/step - loss: 0.0299\n",
      "Epoch 21/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0321\n",
      "Epoch 22/25\n",
      "302/302 [==============================] - 2s 6ms/step - loss: 0.0314\n",
      "Epoch 23/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0306\n",
      "Epoch 24/25\n",
      "302/302 [==============================] - 2s 5ms/step - loss: 0.0245\n",
      "Epoch 25/25\n",
      "302/302 [==============================] - 1s 5ms/step - loss: 0.0297\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x26fc42cde88>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the training data\n",
    "model.fit_generator(generator = train_generator,epochs= 25 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [0.5623669091329196,\n",
       "  0.13048383291085613,\n",
       "  0.12122769348089102,\n",
       "  0.10777592150357904,\n",
       "  0.08091213264862933,\n",
       "  0.06715677720635631,\n",
       "  0.05271967487106662,\n",
       "  0.046065093333485053,\n",
       "  0.03558765192594511,\n",
       "  0.03605050216480196,\n",
       "  0.03080148826380847,\n",
       "  0.032784412671990774,\n",
       "  0.03457335074747997,\n",
       "  0.027949964795809225,\n",
       "  0.0357334597804115,\n",
       "  0.028266922356466082,\n",
       "  0.0315860745050992,\n",
       "  0.03280728152365642,\n",
       "  0.028849190077400767,\n",
       "  0.029919434111954652,\n",
       "  0.032072742346308254,\n",
       "  0.031422854931424216,\n",
       "  0.030591063950438886,\n",
       "  0.024511527828266773,\n",
       "  0.02973125819008627]}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5623669091329196,\n",
       " 0.13048383291085613,\n",
       " 0.12122769348089102,\n",
       " 0.10777592150357904,\n",
       " 0.08091213264862933,\n",
       " 0.06715677720635631,\n",
       " 0.05271967487106662,\n",
       " 0.046065093333485053,\n",
       " 0.03558765192594511,\n",
       " 0.03605050216480196,\n",
       " 0.03080148826380847,\n",
       " 0.032784412671990774,\n",
       " 0.03457335074747997,\n",
       " 0.027949964795809225,\n",
       " 0.0357334597804115,\n",
       " 0.028266922356466082,\n",
       " 0.0315860745050992,\n",
       " 0.03280728152365642,\n",
       " 0.028849190077400767,\n",
       " 0.029919434111954652,\n",
       " 0.032072742346308254,\n",
       " 0.031422854931424216,\n",
       " 0.030591063950438886,\n",
       " 0.024511527828266773,\n",
       " 0.02973125819008627]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.history.history['loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15,5))\n",
    "plt.plot(range(len(model.history.history['loss'])), model.history.history['loss'])\n",
    "plt.title('loss  vs range of loss', {'fontsize' : 20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.73427755],\n",
       "       [1.37555462],\n",
       "       [2.11981412],\n",
       "       [2.5491946 ],\n",
       "       [1.34837864],\n",
       "       [2.06799859],\n",
       "       [1.52629072],\n",
       "       [1.81761723],\n",
       "       [2.01690774],\n",
       "       [2.38070353],\n",
       "       [0.67731312],\n",
       "       [0.9856699 ]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_eval_batch = scaled_train[-12: ] # it will give last 12 records\n",
    "first_eval_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.73427755],\n",
       "        [1.37555462],\n",
       "        [2.11981412],\n",
       "        [2.5491946 ],\n",
       "        [1.34837864],\n",
       "        [2.06799859],\n",
       "        [1.52629072],\n",
       "        [1.81761723],\n",
       "        [2.01690774],\n",
       "        [2.38070353],\n",
       "        [0.67731312],\n",
       "        [0.9856699 ]]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need to reshape this\n",
    "n_input = 12\n",
    "n_features = 1\n",
    "first_eval_batch.reshape((1, n_input, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 1)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_eval_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_eval_batch = first_eval_batch.reshape((1, n_input, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.1684217]], dtype=float32)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict on these 12 points\n",
    "model.predict(first_eval_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Forecast using RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# holding predictions\n",
    "test_predictions = []\n",
    "\n",
    "n_input = 12\n",
    "n_features = 1\n",
    "\n",
    "# last n_input points from the training dataset\n",
    "first_eval_batch = scaled_train[-n_input :]\n",
    "\n",
    "# reshape this as RNN format(same format as time series generator wants)\n",
    "current_batch = first_eval_batch.reshape((1,n_input, n_features))\n",
    "\n",
    "for i in range(len(test)):\n",
    "    current_predictions = model.predict(current_batch)[0]\n",
    "#     print(current_predictions)\n",
    "    test_predictions.append(current_predictions)\n",
    "#     print(test_predictions)\n",
    "\n",
    "    current_batch = np.append(current_batch[0:, 1:, :], [[current_predictions]], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([2.1684217], dtype=float32),\n",
       " array([1.8691804], dtype=float32),\n",
       " array([2.5282564], dtype=float32),\n",
       " array([2.6230407], dtype=float32),\n",
       " array([1.931998], dtype=float32),\n",
       " array([2.5469577], dtype=float32),\n",
       " array([2.1166308], dtype=float32),\n",
       " array([2.452118], dtype=float32),\n",
       " array([2.6602573], dtype=float32),\n",
       " array([2.777379], dtype=float32),\n",
       " array([1.1704305], dtype=float32)]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13679.146],\n",
       "       [12853.304],\n",
       "       [14672.215],\n",
       "       [14933.8  ],\n",
       "       [13026.667],\n",
       "       [14723.827],\n",
       "       [13536.215],\n",
       "       [14462.089],\n",
       "       [15036.51 ],\n",
       "       [15359.741],\n",
       "       [10924.9  ]], dtype=float32)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_predictions = sc.inverse_transform(test_predictions)\n",
    "true_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>12683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>11919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>14138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>14583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>12640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>14257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>13914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>14174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>15504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>10718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales\n",
       "DATE             \n",
       "2018-03-01  12683\n",
       "2018-04-01  11919\n",
       "2018-05-01  14138\n",
       "2018-06-01  14583\n",
       "2018-07-01  12640\n",
       "2018-08-01  14257\n",
       "2018-09-01  12396\n",
       "2018-10-01  13914\n",
       "2018-11-01  14174\n",
       "2018-12-01  15504\n",
       "2019-01-01  10718"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LOKESH\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>sales</th>\n",
       "      <th>predcitions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>12683</td>\n",
       "      <td>13679.146484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>11919</td>\n",
       "      <td>12853.303711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>14138</td>\n",
       "      <td>14672.214844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>14583</td>\n",
       "      <td>14933.799805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>12640</td>\n",
       "      <td>13026.666992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sales   predcitions\n",
       "DATE                           \n",
       "2018-03-01  12683  13679.146484\n",
       "2018-04-01  11919  12853.303711\n",
       "2018-05-01  14138  14672.214844\n",
       "2018-06-01  14583  14933.799805\n",
       "2018-07-01  12640  13026.666992"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['predcitions'] = true_predictions\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.plot(figsize = (15,5), legend = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('mymodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras.models import load_model\n",
    "# new_model = load_model('mymodel.h5')"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}