{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  internetActivity\n",
      "0           1         50.341965\n",
      "1           1         46.150144\n",
      "2           1         35.053423\n",
      "3           1         30.914044\n",
      "4           1         31.978532\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b456c10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#https://github.com/ivanarielcaceres/timeseries-lstm-keras/blob/master/timeseries-prediction.ipynb\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import GRU, Dense\n",
    "from keras.layers import LSTM\n",
    "from keras  import callbacks\n",
    "from keras import optimizers\n",
    "import pandas as pd \n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.read_csv('g1.csv')\n",
    "df=df.drop(['gridID','smsIn','smsOut','callIn','callOut'],axis=1)\n",
    "print(df.head())\n",
    "df.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows: 168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:3: 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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>internetActivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50341.965081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46150.143600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35053.423392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30914.043629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31978.532387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   internetActivity\n",
       "0      50341.965081\n",
       "1      46150.143600\n",
       "2      35053.423392\n",
       "3      30914.043629\n",
       "4      31978.532387"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "columns_to_keep = ['internetActivity']\n",
    "df = df[columns_to_keep]\n",
    "df['internetActivity'] = df['internetActivity'].apply(lambda x: x*1000)\n",
    "print('Total rows: {}'.format(len(df)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>internetActivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>60461.824013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19113.807660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30659.575234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>46150.143600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>59414.298365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76521.612181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112291.185193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       internetActivity\n",
       "count        168.000000\n",
       "mean       60461.824013\n",
       "std        19113.807660\n",
       "min        30659.575234\n",
       "25%        46150.143600\n",
       "50%        59414.298365\n",
       "75%        76521.612181\n",
       "max       112291.185193"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14c446590>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "internetActivity    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null값\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min internetActivity    30659.575234\n",
      "dtype: float64\n",
      "Max internetActivity    112291.185193\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Min', np.min(df))\n",
    "print('Max', np.max(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min 0.0\n",
      "Max 1.0\n"
     ]
    }
   ],
   "source": [
    "dataset = df.astype('float64')\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled = scaler.fit_transform(dataset)\n",
    "print('Min', np.min(scaled))\n",
    "print('Max', np.max(scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.24111236]\n",
      " [0.18976189]\n",
      " [0.05382533]\n",
      " [0.00311728]\n",
      " [0.01615743]\n",
      " [0.        ]\n",
      " [0.10992557]\n",
      " [0.41057335]\n",
      " [0.2916813 ]\n",
      " [0.23773589]\n",
      " [0.25012827]\n",
      " [0.33774603]\n",
      " [0.30072117]\n",
      " [0.25519926]\n",
      " [0.29313967]\n",
      " [0.47124411]\n",
      " [0.47775649]\n",
      " [0.56702197]\n",
      " [0.607746  ]\n",
      " [0.60294416]\n",
      " [0.73335397]\n",
      " [0.73801158]\n",
      " [0.55957094]\n",
      " [0.44668211]]\n"
     ]
    }
   ],
   "source": [
    "print(scaled[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 117\n",
      "test: 51\n"
     ]
    }
   ],
   "source": [
    "#Create RNN\n",
    "train_size = int(len(scaled) * 0.70)\n",
    "test_size = len(scaled - train_size)\n",
    "train, test = scaled[0:train_size, :], scaled[train_size: len(scaled), :]\n",
    "print('train: {}\\ntest: {}'.format(len(train), len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    print(len(dataset), look_back)\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        print(i)\n",
    "        print('X {} to {}'.format(i, i+look_back))\n",
    "        print(a)\n",
    "        print('Y {}'.format(i + look_back))\n",
    "        print(dataset[i + look_back, 0])\n",
    "        dataset[i + look_back, 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return np.array(dataX), np.array(dataY)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "X 26 to 27\n",
      "[0.46694697]\n",
      "Y 27\n",
      "0.3324623175695592\n",
      "27\n",
      "X 27 to 28\n",
      "[0.33246232]\n",
      "Y 28\n",
      "0.16399681181945874\n",
      "28\n",
      "X 28 to 29\n",
      "[0.16399681]\n",
      "Y 29\n",
      "0.12920943497834386\n",
      "29\n",
      "X 29 to 30\n",
      "[0.12920943]\n",
      "Y 30\n",
      "0.028940927125846283\n",
      "30\n",
      "X 30 to 31\n",
      "[0.02894093]\n",
      "Y 31\n",
      "0.008787709599856364\n",
      "31\n",
      "X 31 to 32\n",
      "[0.00878771]\n",
      "Y 32\n",
      "0.0512303609595417\n",
      "32\n",
      "X 32 to 33\n",
      "[0.05123036]\n",
      "Y 33\n",
      "0.05826847426890919\n",
      "33\n",
      "X 33 to 34\n",
      "[0.05826847]\n",
      "Y 34\n",
      "0.1503575507567087\n",
      "34\n",
      "X 34 to 35\n",
      "[0.15035755]\n",
      "Y 35\n",
      "0.357964360884446\n",
      "35\n",
      "X 35 to 36\n",
      "[0.35796436]\n",
      "Y 36\n",
      "0.49583652061068156\n",
      "36\n",
      "X 36 to 37\n",
      "[0.49583652]\n",
      "Y 37\n",
      "0.6257317716270365\n",
      "37\n",
      "X 37 to 38\n",
      "[0.62573177]\n",
      "Y 38\n",
      "0.570891164493458\n",
      "38\n",
      "X 38 to 39\n",
      "[0.57089116]\n",
      "Y 39\n",
      "0.5415014301053864\n",
      "39\n",
      "X 39 to 40\n",
      "[0.54150143]\n",
      "Y 40\n",
      "0.5824810016051338\n",
      "40\n",
      "X 40 to 41\n",
      "[0.582481]\n",
      "Y 41\n",
      "0.725289022389892\n",
      "41\n",
      "X 41 to 42\n",
      "[0.72528902]\n",
      "Y 42\n",
      "0.6972960942919606\n",
      "42\n",
      "X 42 to 43\n",
      "[0.69729609]\n",
      "Y 43\n",
      "0.48451597732341806\n",
      "43\n",
      "X 43 to 44\n",
      "[0.48451598]\n",
      "Y 44\n",
      "0.5590311695620611\n",
      "44\n",
      "X 44 to 45\n",
      "[0.55903117]\n",
      "Y 45\n",
      "0.7050350480088428\n",
      "45\n",
      "X 45 to 46\n",
      "[0.70503505]\n",
      "Y 46\n",
      "0.6031811576722008\n",
      "46\n",
      "X 46 to 47\n",
      "[0.60318116]\n",
      "Y 47\n",
      "0.5075434299310404\n",
      "47\n",
      "X 47 to 48\n",
      "[0.50754343]\n",
      "Y 48\n",
      "0.37654307497093914\n",
      "48\n",
      "X 48 to 49\n",
      "[0.37654307]\n",
      "Y 49\n",
      "0.3465353579340424\n"
     ]
    }
   ],
   "source": [
    "look_back = 1\n",
    "X_train, y_train = create_dataset(train, look_back)\n",
    "X_test, y_test = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(115, 1, 1)\n",
      "(49, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.0621\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.0379\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.0329\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.0319\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.0304\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.0272\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0278\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.0246\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0240\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0212\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0189\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0178\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0148\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0146\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0156\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0142\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0137\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0124\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0123\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0119\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0118\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0121\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0115\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0112\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0111\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0114\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0113\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0116\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0117\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0110\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0115\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x149f27410>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size = 1\n",
    "model = Sequential()\n",
    "model.add(LSTM(4, batch_input_shape=(batch_size, look_back, 1), stateful=True))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=batch_size, verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 8396.39 RMSE\n",
      "Test Score: 9733.91 RMSE\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "trainPredict = model.predict(X_train, batch_size=batch_size)\n",
    "model.reset_states()\n",
    "\n",
    "testPredict = model.predict(X_test, batch_size=batch_size)\n",
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "y_train = scaler.inverse_transform([y_train])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "y_test = scaler.inverse_transform([y_test])\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(y_train[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(y_test[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainPredictPlot = np.empty_like(scaled)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(scaled)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(scaled)-1, :] = testPredict\n",
    "# plot baseline and predictions\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(scaler.inverse_transform(scaled))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}