{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   internetActivity\n",
      "0         57.799009\n",
      "1         44.046899\n",
      "2         41.207149\n",
      "3         33.022070\n",
      "4         31.376930\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14d6051d0>"
      ]
     },
     "execution_count": 12,
     "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",
    "df = pd.read_csv('grid1.csv')\n",
    "df=df.drop(['Unnamed: 0','smsIn','smsOut','callIn','callOut'],axis=1)\n",
    "print(df.head())\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total rows: 24\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>internetActivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57799.009468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44046.899011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41207.149433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33022.069709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31376.930116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   internetActivity\n",
       "0      57799.009468\n",
       "1      44046.899011\n",
       "2      41207.149433\n",
       "3      33022.069709\n",
       "4      31376.930116"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "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>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62793.681200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19011.375586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>31376.930116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43768.550301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65804.393588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77498.864368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89866.085817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       internetActivity\n",
       "count         24.000000\n",
       "mean       62793.681200\n",
       "std        19011.375586\n",
       "min        31376.930116\n",
       "25%        43768.550301\n",
       "50%        65804.393588\n",
       "75%        77498.864368\n",
       "max        89866.085817"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14d7cea50>"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "internetActivity    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null값\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min internetActivity    31376.930116\n",
      "dtype: float64\n",
      "Max internetActivity    89866.085817\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Min', np.min(df))\n",
    "print('Max', np.max(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min 0.0\n",
      "Max 1.0\n"
     ]
    }
   ],
   "source": [
    "dataset = df.astype('float32')\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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.4517432 ]\n",
      " [0.2166208 ]\n",
      " [0.16806906]\n",
      " [0.02812725]\n",
      " [0.        ]\n",
      " [0.05923599]\n",
      " [0.0690589 ]\n",
      " [0.19758493]\n",
      " [0.48733562]\n",
      " [0.6797598 ]\n",
      " [0.8610509 ]\n",
      " [0.7845115 ]\n",
      " [0.74349314]\n",
      " [0.800687  ]\n",
      " [1.        ]\n",
      " [0.9609311 ]\n",
      " [0.66396004]\n",
      " [0.7679587 ]\n",
      " [0.9717321 ]\n",
      " [0.82957774]\n",
      " [0.69609886]\n",
      " [0.51326555]\n",
      " [0.47138458]\n",
      " [0.46912688]]\n"
     ]
    }
   ],
   "source": [
    "print(scaled[:24])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 16\n",
      "test: 8\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": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 1\n",
      "0\n",
      "X 0 to 1\n",
      "[0.4517432]\n",
      "Y 1\n",
      "0.2166208\n",
      "1\n",
      "X 1 to 2\n",
      "[0.2166208]\n",
      "Y 2\n",
      "0.16806906\n",
      "2\n",
      "X 2 to 3\n",
      "[0.16806906]\n",
      "Y 3\n",
      "0.028127253\n",
      "3\n",
      "X 3 to 4\n",
      "[0.02812725]\n",
      "Y 4\n",
      "0.0\n",
      "4\n",
      "X 4 to 5\n",
      "[0.]\n",
      "Y 5\n",
      "0.05923599\n",
      "5\n",
      "X 5 to 6\n",
      "[0.05923599]\n",
      "Y 6\n",
      "0.069058895\n",
      "6\n",
      "X 6 to 7\n",
      "[0.0690589]\n",
      "Y 7\n",
      "0.19758493\n",
      "7\n",
      "X 7 to 8\n",
      "[0.19758493]\n",
      "Y 8\n",
      "0.48733562\n",
      "8\n",
      "X 8 to 9\n",
      "[0.48733562]\n",
      "Y 9\n",
      "0.6797598\n",
      "9\n",
      "X 9 to 10\n",
      "[0.6797598]\n",
      "Y 10\n",
      "0.8610509\n",
      "10\n",
      "X 10 to 11\n",
      "[0.8610509]\n",
      "Y 11\n",
      "0.7845115\n",
      "11\n",
      "X 11 to 12\n",
      "[0.7845115]\n",
      "Y 12\n",
      "0.74349314\n",
      "12\n",
      "X 12 to 13\n",
      "[0.74349314]\n",
      "Y 13\n",
      "0.800687\n",
      "13\n",
      "X 13 to 14\n",
      "[0.800687]\n",
      "Y 14\n",
      "1.0\n",
      "8 1\n",
      "0\n",
      "X 0 to 1\n",
      "[0.66396004]\n",
      "Y 1\n",
      "0.7679587\n",
      "1\n",
      "X 1 to 2\n",
      "[0.7679587]\n",
      "Y 2\n",
      "0.9717321\n",
      "2\n",
      "X 2 to 3\n",
      "[0.9717321]\n",
      "Y 3\n",
      "0.82957774\n",
      "3\n",
      "X 3 to 4\n",
      "[0.82957774]\n",
      "Y 4\n",
      "0.69609886\n",
      "4\n",
      "X 4 to 5\n",
      "[0.69609886]\n",
      "Y 5\n",
      "0.51326555\n",
      "5\n",
      "X 5 to 6\n",
      "[0.51326555]\n",
      "Y 6\n",
      "0.47138458\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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 1, 1)\n",
      "(6, 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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 0s - loss: 0.1910\n",
      "Epoch 2/100\n",
      " - 0s - loss: 0.1610\n",
      "Epoch 3/100\n",
      " - 0s - loss: 0.1421\n",
      "Epoch 4/100\n",
      " - 0s - loss: 0.1343\n",
      "Epoch 5/100\n",
      " - 0s - loss: 0.1090\n",
      "Epoch 6/100\n",
      " - 0s - loss: 0.1179\n",
      "Epoch 7/100\n",
      " - 0s - loss: 0.0915\n",
      "Epoch 8/100\n",
      " - 0s - loss: 0.1026\n",
      "Epoch 9/100\n",
      " - 0s - loss: 0.0965\n",
      "Epoch 10/100\n",
      " - 0s - loss: 0.0856\n",
      "Epoch 11/100\n",
      " - 0s - loss: 0.0922\n",
      "Epoch 12/100\n",
      " - 0s - loss: 0.0775\n",
      "Epoch 13/100\n",
      " - 0s - loss: 0.0929\n",
      "Epoch 14/100\n",
      " - 0s - loss: 0.0879\n",
      "Epoch 15/100\n",
      " - 0s - loss: 0.0938\n",
      "Epoch 16/100\n",
      " - 0s - loss: 0.0921\n",
      "Epoch 17/100\n",
      " - 0s - loss: 0.0735\n",
      "Epoch 18/100\n",
      " - 0s - loss: 0.0803\n",
      "Epoch 19/100\n",
      " - 0s - loss: 0.0862\n",
      "Epoch 20/100\n",
      " - 0s - loss: 0.0821\n",
      "Epoch 21/100\n",
      " - 0s - loss: 0.0853\n",
      "Epoch 22/100\n",
      " - 0s - loss: 0.0798\n",
      "Epoch 23/100\n",
      " - 0s - loss: 0.0847\n",
      "Epoch 24/100\n",
      " - 0s - loss: 0.0506\n",
      "Epoch 25/100\n",
      " - 0s - loss: 0.0864\n",
      "Epoch 26/100\n",
      " - 0s - loss: 0.0748\n",
      "Epoch 27/100\n",
      " - 0s - loss: 0.0666\n",
      "Epoch 28/100\n",
      " - 0s - loss: 0.0774\n",
      "Epoch 29/100\n",
      " - 0s - loss: 0.0784\n",
      "Epoch 30/100\n",
      " - 0s - loss: 0.0708\n",
      "Epoch 31/100\n",
      " - 0s - loss: 0.0730\n",
      "Epoch 32/100\n",
      " - 0s - loss: 0.0779\n",
      "Epoch 33/100\n",
      " - 0s - loss: 0.0746\n",
      "Epoch 34/100\n",
      " - 0s - loss: 0.0738\n",
      "Epoch 35/100\n",
      " - 0s - loss: 0.0536\n",
      "Epoch 36/100\n",
      " - 0s - loss: 0.0608\n",
      "Epoch 37/100\n",
      " - 0s - loss: 0.0626\n",
      "Epoch 38/100\n",
      " - 0s - loss: 0.0820\n",
      "Epoch 39/100\n",
      " - 0s - loss: 0.0729\n",
      "Epoch 40/100\n",
      " - 0s - loss: 0.0671\n",
      "Epoch 41/100\n",
      " - 0s - loss: 0.0628\n",
      "Epoch 42/100\n",
      " - 0s - loss: 0.0640\n",
      "Epoch 43/100\n",
      " - 0s - loss: 0.0594\n",
      "Epoch 44/100\n",
      " - 0s - loss: 0.0649\n",
      "Epoch 45/100\n",
      " - 0s - loss: 0.0454\n",
      "Epoch 46/100\n",
      " - 0s - loss: 0.0417\n",
      "Epoch 47/100\n",
      " - 0s - loss: 0.0585\n",
      "Epoch 48/100\n",
      " - 0s - loss: 0.0502\n",
      "Epoch 49/100\n",
      " - 0s - loss: 0.0505\n",
      "Epoch 50/100\n",
      " - 0s - loss: 0.0438\n",
      "Epoch 51/100\n",
      " - 0s - loss: 0.0470\n",
      "Epoch 52/100\n",
      " - 0s - loss: 0.0543\n",
      "Epoch 53/100\n",
      " - 0s - loss: 0.0514\n",
      "Epoch 54/100\n",
      " - 0s - loss: 0.0620\n",
      "Epoch 55/100\n",
      " - 0s - loss: 0.0621\n",
      "Epoch 56/100\n",
      " - 0s - loss: 0.0523\n",
      "Epoch 57/100\n",
      " - 0s - loss: 0.0449\n",
      "Epoch 58/100\n",
      " - 0s - loss: 0.0469\n",
      "Epoch 59/100\n",
      " - 0s - loss: 0.0437\n",
      "Epoch 60/100\n",
      " - 0s - loss: 0.0416\n",
      "Epoch 61/100\n",
      " - 0s - loss: 0.0443\n",
      "Epoch 62/100\n",
      " - 0s - loss: 0.0520\n",
      "Epoch 63/100\n",
      " - 0s - loss: 0.0266\n",
      "Epoch 64/100\n",
      " - 0s - loss: 0.0336\n",
      "Epoch 65/100\n",
      " - 0s - loss: 0.0378\n",
      "Epoch 66/100\n",
      " - 0s - loss: 0.0413\n",
      "Epoch 67/100\n",
      " - 0s - loss: 0.0326\n",
      "Epoch 68/100\n",
      " - 0s - loss: 0.0527\n",
      "Epoch 69/100\n",
      " - 0s - loss: 0.0404\n",
      "Epoch 70/100\n",
      " - 0s - loss: 0.0337\n",
      "Epoch 71/100\n",
      " - 0s - loss: 0.0273\n",
      "Epoch 72/100\n",
      " - 0s - loss: 0.0281\n",
      "Epoch 73/100\n",
      " - 0s - loss: 0.0311\n",
      "Epoch 74/100\n",
      " - 0s - loss: 0.0369\n",
      "Epoch 75/100\n",
      " - 0s - loss: 0.0365\n",
      "Epoch 76/100\n",
      " - 0s - loss: 0.0256\n",
      "Epoch 77/100\n",
      " - 0s - loss: 0.0287\n",
      "Epoch 78/100\n",
      " - 0s - loss: 0.0323\n",
      "Epoch 79/100\n",
      " - 0s - loss: 0.0320\n",
      "Epoch 80/100\n",
      " - 0s - loss: 0.0333\n",
      "Epoch 81/100\n",
      " - 0s - loss: 0.0301\n",
      "Epoch 82/100\n",
      " - 0s - loss: 0.0340\n",
      "Epoch 83/100\n",
      " - 0s - loss: 0.0265\n",
      "Epoch 84/100\n",
      " - 0s - loss: 0.0259\n",
      "Epoch 85/100\n",
      " - 0s - loss: 0.0244\n",
      "Epoch 86/100\n",
      " - 0s - loss: 0.0266\n",
      "Epoch 87/100\n",
      " - 0s - loss: 0.0304\n",
      "Epoch 88/100\n",
      " - 0s - loss: 0.0266\n",
      "Epoch 89/100\n",
      " - 0s - loss: 0.0226\n",
      "Epoch 90/100\n",
      " - 0s - loss: 0.0212\n",
      "Epoch 91/100\n",
      " - 0s - loss: 0.0247\n",
      "Epoch 92/100\n",
      " - 0s - loss: 0.0239\n",
      "Epoch 93/100\n",
      " - 0s - loss: 0.0276\n",
      "Epoch 94/100\n",
      " - 0s - loss: 0.0304\n",
      "Epoch 95/100\n",
      " - 0s - loss: 0.0206\n",
      "Epoch 96/100\n",
      " - 0s - loss: 0.0235\n",
      "Epoch 97/100\n",
      " - 0s - loss: 0.0207\n",
      "Epoch 98/100\n",
      " - 0s - loss: 0.0284\n",
      "Epoch 99/100\n",
      " - 0s - loss: 0.0217\n",
      "Epoch 100/100\n",
      " - 0s - loss: 0.0173\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x14da499d0>"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 9396.37 RMSE\n",
      "Test Score: 13194.16 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": 29,
   "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
}