Stock Prediction Using LSTM.ipynb

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   "source": [
    "### Stock Market Prediction And Forecasting Using Stacked LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('AAPL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>low</th>\n",
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       "      <th>volume</th>\n",
       "      <th>adjClose</th>\n",
       "      <th>adjHigh</th>\n",
       "      <th>adjLow</th>\n",
       "      <th>adjOpen</th>\n",
       "      <th>adjVolume</th>\n",
       "      <th>divCash</th>\n",
       "      <th>splitFactor</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-05-27 00:00:00+00:00</td>\n",
       "      <td>132.045</td>\n",
       "      <td>132.260</td>\n",
       "      <td>130.05</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-05-28 00:00:00+00:00</td>\n",
       "      <td>131.780</td>\n",
       "      <td>131.950</td>\n",
       "      <td>131.10</td>\n",
       "      <td>131.86</td>\n",
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       "      <td>121.438354</td>\n",
       "      <td>121.595013</td>\n",
       "      <td>120.811718</td>\n",
       "      <td>121.512076</td>\n",
       "      <td>30733309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-05-29 00:00:00+00:00</td>\n",
       "      <td>130.280</td>\n",
       "      <td>131.450</td>\n",
       "      <td>129.90</td>\n",
       "      <td>131.23</td>\n",
       "      <td>50884452</td>\n",
       "      <td>120.056069</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-01 00:00:00+00:00</td>\n",
       "      <td>130.535</td>\n",
       "      <td>131.390</td>\n",
       "      <td>130.05</td>\n",
       "      <td>131.20</td>\n",
       "      <td>32112797</td>\n",
       "      <td>120.291057</td>\n",
       "      <td>121.078960</td>\n",
       "      <td>119.844118</td>\n",
       "      <td>120.903870</td>\n",
       "      <td>32112797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-06-02 00:00:00+00:00</td>\n",
       "      <td>129.960</td>\n",
       "      <td>130.655</td>\n",
       "      <td>129.32</td>\n",
       "      <td>129.86</td>\n",
       "      <td>33667627</td>\n",
       "      <td>119.761181</td>\n",
       "      <td>120.401640</td>\n",
       "      <td>119.171406</td>\n",
       "      <td>119.669029</td>\n",
       "      <td>33667627</td>\n",
       "      <td>0.0</td>\n",
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       "   Unnamed: 0 symbol                       date    close     high     low  \\\n",
       "0           0   AAPL  2015-05-27 00:00:00+00:00  132.045  132.260  130.05   \n",
       "1           1   AAPL  2015-05-28 00:00:00+00:00  131.780  131.950  131.10   \n",
       "2           2   AAPL  2015-05-29 00:00:00+00:00  130.280  131.450  129.90   \n",
       "3           3   AAPL  2015-06-01 00:00:00+00:00  130.535  131.390  130.05   \n",
       "4           4   AAPL  2015-06-02 00:00:00+00:00  129.960  130.655  129.32   \n",
       "\n",
       "     open    volume    adjClose     adjHigh      adjLow     adjOpen  \\\n",
       "0  130.34  45833246  121.682558  121.880685  119.844118  120.111360   \n",
       "1  131.86  30733309  121.438354  121.595013  120.811718  121.512076   \n",
       "2  131.23  50884452  120.056069  121.134251  119.705890  120.931516   \n",
       "3  131.20  32112797  120.291057  121.078960  119.844118  120.903870   \n",
       "4  129.86  33667627  119.761181  120.401640  119.171406  119.669029   \n",
       "\n",
       "   adjVolume  divCash  splitFactor  \n",
       "0   45833246      0.0          1.0  \n",
       "1   30733309      0.0          1.0  \n",
       "2   50884452      0.0          1.0  \n",
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   "source": [
    "df.head()"
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   "execution_count": 4,
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       "      <th>1253</th>\n",
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       "      <td>AAPL</td>\n",
       "      <td>2020-05-18 00:00:00+00:00</td>\n",
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       "      <th>1254</th>\n",
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       "      <td>2020-05-19 00:00:00+00:00</td>\n",
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       "      <td>2020-05-21 00:00:00+00:00</td>\n",
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       "      <td>AAPL</td>\n",
       "      <td>2020-05-22 00:00:00+00:00</td>\n",
       "      <td>318.89</td>\n",
       "      <td>319.23</td>\n",
       "      <td>315.3500</td>\n",
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       "      <td>318.89</td>\n",
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       "      Unnamed: 0 symbol                       date   close    high       low  \\\n",
       "1253        1253   AAPL  2020-05-18 00:00:00+00:00  314.96  316.50  310.3241   \n",
       "1254        1254   AAPL  2020-05-19 00:00:00+00:00  313.14  318.52  313.0100   \n",
       "1255        1255   AAPL  2020-05-20 00:00:00+00:00  319.23  319.52  316.2000   \n",
       "1256        1256   AAPL  2020-05-21 00:00:00+00:00  316.85  320.89  315.8700   \n",
       "1257        1257   AAPL  2020-05-22 00:00:00+00:00  318.89  319.23  315.3500   \n",
       "\n",
       "        open    volume  adjClose  adjHigh    adjLow  adjOpen  adjVolume  \\\n",
       "1253  313.17  33843125    314.96   316.50  310.3241   313.17   33843125   \n",
       "1254  315.03  25432385    313.14   318.52  313.0100   315.03   25432385   \n",
       "1255  316.68  27876215    319.23   319.52  316.2000   316.68   27876215   \n",
       "1256  318.66  25672211    316.85   320.89  315.8700   318.66   25672211   \n",
       "1257  315.77  20450754    318.89   319.23  315.3500   315.77   20450754   \n",
       "\n",
       "      divCash  splitFactor  \n",
       "1253      0.0          1.0  \n",
       "1254      0.0          1.0  \n",
       "1255      0.0          1.0  \n",
       "1256      0.0          1.0  \n",
       "1257      0.0          1.0  "
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    "df.tail()"
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   "source": [
    "df1=df.reset_index()['high']"
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       "0       132.260\n",
       "1       131.950\n",
       "2       131.450\n",
       "3       131.390\n",
       "4       130.655\n",
       "         ...   \n",
       "1253    316.500\n",
       "1254    318.520\n",
       "1255    319.520\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### LSTM are sensitive to the scale of the data. so we apply MinMax scaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       132.260\n",
       "1       131.950\n",
       "2       131.450\n",
       "3       131.390\n",
       "4       130.655\n",
       "         ...   \n",
       "1253    316.500\n",
       "1254    318.520\n",
       "1255    319.520\n",
       "1256    320.890\n",
       "1257    319.230\n",
       "Name: high, Length: 1258, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "df1=scaler.fit_transform(np.array(df1).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.17186045],\n",
       "       [0.17054789],\n",
       "       [0.16843086],\n",
       "       ...,\n",
       "       [0.96473029],\n",
       "       [0.97053095],\n",
       "       [0.96350241]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "##splitting dataset into train and test split\n",
    "training_size=int(len(df1)*0.65)\n",
    "test_size=len(df1)-training_size\n",
    "train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(817, 441)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_size,test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.17186045],\n",
       "       [0.17054789],\n",
       "       [0.16843086],\n",
       "       [0.16817681],\n",
       "       [0.16506478],\n",
       "       [0.16627149],\n",
       "       [0.16474723],\n",
       "       [0.16097891],\n",
       "       [0.15894657],\n",
       "       [0.15416208],\n",
       "       [0.15949699],\n",
       "       [0.1630536 ],\n",
       "       [0.15522059],\n",
       "       [0.15060547],\n",
       "       [0.15318825],\n",
       "       [0.15331527],\n",
       "       [0.15513591],\n",
       "       [0.15306122],\n",
       "       [0.1540774 ],\n",
       "       [0.15217207],\n",
       "       [0.16144466],\n",
       "       [0.15890423],\n",
       "       [0.15378101],\n",
       "       [0.14734525],\n",
       "       [0.14586332],\n",
       "       [0.14933525],\n",
       "       [0.14827674],\n",
       "       [0.14632907],\n",
       "       [0.14599035],\n",
       "       [0.13959692],\n",
       "       [0.13714116],\n",
       "       [0.13625201],\n",
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       "       [0.494157  ],\n",
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       "       [0.49174359],\n",
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       "       [0.49720552],\n",
       "       [0.505047  ],\n",
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       "       [0.53145906]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "# convert an array of values into a dataset matrix\n",
    "def create_dataset(dataset, time_step=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-time_step-1):\n",
    "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, X=0,1,2,3-----99   Y=100 \n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + time_step, 0])\n",
    "\treturn numpy.array(dataX), numpy.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape into X=t,t+1,t+2..t+99 and Y=t+100\n",
    "\n",
    "time_step = 100\n",
    "X_train, y_train = create_dataset(train_data, time_step)\n",
    "X_test, ytest = create_dataset(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(716, 100)\n",
      "(716,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape), print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(340, 100)\n",
      "(340,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test.shape), print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
    "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
    "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create the Stacked LSTM model\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))\n",
    "model.add(LSTM(50,return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error',optimizer='adam')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "12/12 [==============================] - 7s 274ms/step - loss: 0.0332 - val_loss: 0.0550\n",
      "Epoch 2/100\n",
      "12/12 [==============================] - 1s 112ms/step - loss: 0.0049 - val_loss: 0.0047\n",
      "Epoch 3/100\n",
      "12/12 [==============================] - 1s 96ms/step - loss: 0.0014 - val_loss: 0.0036\n",
      "Epoch 4/100\n",
      "12/12 [==============================] - 1s 95ms/step - loss: 7.3891e-04 - val_loss: 0.0041\n",
      "Epoch 5/100\n",
      "12/12 [==============================] - 1s 97ms/step - loss: 7.4173e-04 - val_loss: 0.0036\n",
      "Epoch 6/100\n",
      "12/12 [==============================] - 1s 99ms/step - loss: 5.9581e-04 - val_loss: 0.0032\n",
      "Epoch 7/100\n",
      "12/12 [==============================] - 1s 99ms/step - loss: 5.6968e-04 - val_loss: 0.0032\n",
      "Epoch 8/100\n",
      "12/12 [==============================] - 1s 99ms/step - loss: 5.4553e-04 - val_loss: 0.0032\n",
      "Epoch 9/100\n",
      "12/12 [==============================] - 1s 98ms/step - loss: 5.3993e-04 - val_loss: 0.0031\n",
      "Epoch 10/100\n",
      "12/12 [==============================] - 1s 100ms/step - loss: 5.3169e-04 - val_loss: 0.0030\n",
      "Epoch 11/100\n",
      "12/12 [==============================] - 1s 101ms/step - loss: 5.2998e-04 - val_loss: 0.0030\n",
      "Epoch 12/100\n",
      "12/12 [==============================] - 1s 99ms/step - loss: 5.3182e-04 - val_loss: 0.0029\n",
      "Epoch 13/100\n",
      "12/12 [==============================] - 1s 99ms/step - loss: 5.3085e-04 - val_loss: 0.0028\n",
      "Epoch 14/100\n",
      "12/12 [==============================] - 1s 101ms/step - loss: 4.6892e-04 - val_loss: 0.0029\n",
      "Epoch 15/100\n",
      "12/12 [==============================] - 1s 101ms/step - loss: 5.1843e-04 - val_loss: 0.0027\n",
      "Epoch 16/100\n",
      "12/12 [==============================] - 1s 101ms/step - loss: 5.3209e-04 - val_loss: 0.0026\n",
      "Epoch 17/100\n",
      "12/12 [==============================] - 1s 101ms/step - loss: 4.8373e-04 - val_loss: 0.0025\n",
      "Epoch 18/100\n",
      "12/12 [==============================] - 1s 101ms/step - loss: 4.8677e-04 - val_loss: 0.0025\n",
      "Epoch 19/100\n",
      "12/12 [==============================] - 1s 103ms/step - loss: 4.7914e-04 - val_loss: 0.0024\n",
      "Epoch 20/100\n",
      "12/12 [==============================] - 1s 102ms/step - loss: 4.3812e-04 - val_loss: 0.0023\n",
      "Epoch 21/100\n",
      "12/12 [==============================] - 1s 102ms/step - loss: 4.3960e-04 - val_loss: 0.0023\n",
      "Epoch 22/100\n",
      "12/12 [==============================] - 1s 104ms/step - loss: 4.4730e-04 - val_loss: 0.0022\n",
      "Epoch 23/100\n",
      "12/12 [==============================] - 1s 106ms/step - loss: 4.5165e-04 - val_loss: 0.0020\n",
      "Epoch 24/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 4.6102e-04 - val_loss: 0.0026\n",
      "Epoch 25/100\n",
      "12/12 [==============================] - 1s 102ms/step - loss: 4.6428e-04 - val_loss: 0.0019\n",
      "Epoch 26/100\n",
      "12/12 [==============================] - 1s 104ms/step - loss: 3.9415e-04 - val_loss: 0.0019\n",
      "Epoch 27/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 4.1409e-04 - val_loss: 0.0020\n",
      "Epoch 28/100\n",
      "12/12 [==============================] - 1s 104ms/step - loss: 4.4417e-04 - val_loss: 0.0018\n",
      "Epoch 29/100\n",
      "12/12 [==============================] - 1s 103ms/step - loss: 3.9113e-04 - val_loss: 0.0018\n",
      "Epoch 30/100\n",
      "12/12 [==============================] - 1s 104ms/step - loss: 4.4258e-04 - val_loss: 0.0016\n",
      "Epoch 31/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 4.0623e-04 - val_loss: 0.0018\n",
      "Epoch 32/100\n",
      "12/12 [==============================] - 1s 104ms/step - loss: 3.6871e-04 - val_loss: 0.0018\n",
      "Epoch 33/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 4.7080e-04 - val_loss: 0.0015\n",
      "Epoch 34/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 3.6735e-04 - val_loss: 0.0018\n",
      "Epoch 35/100\n",
      "12/12 [==============================] - ETA: 0s - loss: 4.1164e-0 - 1s 107ms/step - loss: 4.1089e-04 - val_loss: 0.0017\n",
      "Epoch 36/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 4.1429e-04 - val_loss: 0.0023\n",
      "Epoch 37/100\n",
      "12/12 [==============================] - 1s 104ms/step - loss: 3.7407e-04 - val_loss: 0.0022\n",
      "Epoch 38/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 4.0628e-04 - val_loss: 0.0013\n",
      "Epoch 39/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 3.5709e-04 - val_loss: 0.0013\n",
      "Epoch 40/100\n",
      "12/12 [==============================] - 1s 106ms/step - loss: 3.3488e-04 - val_loss: 0.0016\n",
      "Epoch 41/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 3.4221e-04 - val_loss: 0.0013\n",
      "Epoch 42/100\n",
      "12/12 [==============================] - 1s 106ms/step - loss: 3.1824e-04 - val_loss: 0.0019\n",
      "Epoch 43/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 3.6161e-04 - val_loss: 0.0019\n",
      "Epoch 44/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 3.2287e-04 - val_loss: 0.0015\n",
      "Epoch 45/100\n",
      "12/12 [==============================] - 1s 106ms/step - loss: 3.5511e-04 - val_loss: 0.0019\n",
      "Epoch 46/100\n",
      "12/12 [==============================] - 1s 105ms/step - loss: 3.5004e-04 - val_loss: 0.0013\n",
      "Epoch 47/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 3.5401e-04 - val_loss: 0.0019\n",
      "Epoch 48/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 3.0945e-04 - val_loss: 0.0012\n",
      "Epoch 49/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 3.5845e-04 - val_loss: 0.0012\n",
      "Epoch 50/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 3.2476e-04 - val_loss: 0.0036\n",
      "Epoch 51/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 3.5470e-04 - val_loss: 0.0015\n",
      "Epoch 52/100\n",
      "12/12 [==============================] - 1s 106ms/step - loss: 3.6825e-04 - val_loss: 0.0033\n",
      "Epoch 53/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 3.7733e-04 - val_loss: 0.0014\n",
      "Epoch 54/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 2.8106e-04 - val_loss: 0.0017\n",
      "Epoch 55/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.9731e-04 - val_loss: 0.0012\n",
      "Epoch 56/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 2.9163e-04 - val_loss: 0.0013\n",
      "Epoch 57/100\n",
      "12/12 [==============================] - 1s 106ms/step - loss: 2.8305e-04 - val_loss: 0.0011\n",
      "Epoch 58/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.7385e-04 - val_loss: 0.0015\n",
      "Epoch 59/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 3.1793e-04 - val_loss: 0.0011\n",
      "Epoch 60/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 2.9582e-04 - val_loss: 0.0015\n",
      "Epoch 61/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 2.8468e-04 - val_loss: 0.0013\n",
      "Epoch 62/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 2.7201e-04 - val_loss: 0.0011\n",
      "Epoch 63/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 2.4676e-04 - val_loss: 0.0015\n",
      "Epoch 64/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 2.4227e-04 - val_loss: 0.0013\n",
      "Epoch 65/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 2.3701e-04 - val_loss: 0.0013\n",
      "Epoch 66/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 2.6451e-04 - val_loss: 0.0013\n",
      "Epoch 67/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 2.3184e-04 - val_loss: 0.0011\n",
      "Epoch 68/100\n",
      "12/12 [==============================] - 1s 107ms/step - loss: 2.4423e-04 - val_loss: 0.0012\n",
      "Epoch 69/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.2500e-04 - val_loss: 0.0011\n",
      "Epoch 70/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 2.3475e-04 - val_loss: 0.0014\n",
      "Epoch 71/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.3001e-04 - val_loss: 0.0012\n",
      "Epoch 72/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 2.1234e-04 - val_loss: 0.0012\n",
      "Epoch 73/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 2.0824e-04 - val_loss: 0.0012\n",
      "Epoch 74/100\n",
      "12/12 [==============================] - 1s 112ms/step - loss: 2.2783e-04 - val_loss: 0.0021\n",
      "Epoch 75/100\n",
      "12/12 [==============================] - 1s 113ms/step - loss: 2.9693e-04 - val_loss: 0.0010\n",
      "Epoch 76/100\n",
      "12/12 [==============================] - 1s 112ms/step - loss: 2.1823e-04 - val_loss: 0.0019\n",
      "Epoch 77/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.5776e-04 - val_loss: 0.0010\n",
      "Epoch 78/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 1.7910e-04 - val_loss: 0.0012\n",
      "Epoch 79/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 1.8172e-04 - val_loss: 9.8794e-04\n",
      "Epoch 80/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 2.0490e-04 - val_loss: 0.0012\n",
      "Epoch 81/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 1.8705e-04 - val_loss: 0.0011\n",
      "Epoch 82/100\n",
      "12/12 [==============================] - 1s 111ms/step - loss: 1.7449e-04 - val_loss: 0.0015\n",
      "Epoch 83/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 1.8202e-04 - val_loss: 0.0016\n",
      "Epoch 84/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.2344e-04 - val_loss: 9.4405e-04\n",
      "Epoch 85/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 1.7741e-04 - val_loss: 0.0011\n",
      "Epoch 86/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 2.2451e-04 - val_loss: 0.0021\n",
      "Epoch 87/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 1.9979e-04 - val_loss: 9.6677e-04\n",
      "Epoch 88/100\n",
      "12/12 [==============================] - 1s 108ms/step - loss: 1.7458e-04 - val_loss: 0.0015\n",
      "Epoch 89/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 1.7682e-04 - val_loss: 9.0862e-04\n",
      "Epoch 90/100\n",
      "12/12 [==============================] - 1s 111ms/step - loss: 1.6369e-04 - val_loss: 8.8118e-04\n",
      "Epoch 91/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 1.6032e-04 - val_loss: 8.6823e-04\n",
      "Epoch 92/100\n",
      "12/12 [==============================] - 1s 110ms/step - loss: 1.6462e-04 - val_loss: 8.5513e-04\n",
      "Epoch 93/100\n",
      "12/12 [==============================] - 1s 112ms/step - loss: 2.0099e-04 - val_loss: 0.0015\n",
      "Epoch 94/100\n",
      "12/12 [==============================] - 1s 111ms/step - loss: 1.8067e-04 - val_loss: 8.7963e-04\n",
      "Epoch 95/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 1.6424e-04 - val_loss: 8.5974e-04\n",
      "Epoch 96/100\n",
      "12/12 [==============================] - 1s 111ms/step - loss: 1.6050e-04 - val_loss: 8.4123e-04\n",
      "Epoch 97/100\n",
      "12/12 [==============================] - 1s 112ms/step - loss: 1.6915e-04 - val_loss: 0.0014\n",
      "Epoch 98/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 1.8094e-04 - val_loss: 8.9654e-04\n",
      "Epoch 99/100\n",
      "12/12 [==============================] - 1s 109ms/step - loss: 1.5584e-04 - val_loss: 0.0012\n",
      "Epoch 100/100\n",
      "12/12 [==============================] - 1s 111ms/step - loss: 1.7466e-04 - val_loss: 8.8691e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x25cc94d0130>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.4.1'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Lets Do the prediction and check performance metrics\n",
    "train_predict=model.predict(X_train)\n",
    "test_predict=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Transformback to original form\n",
    "train_predict=scaler.inverse_transform(train_predict)\n",
    "test_predict=scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.64575596318937"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Calculate RMSE performance metrics\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "math.sqrt(mean_squared_error(y_train,train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238.3066998748922"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Test Data RMSE\n",
    "math.sqrt(mean_squared_error(ytest,test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "### Plotting \n",
    "# shift train predictions for plotting\n",
    "look_back=100\n",
    "trainPredictPlot = numpy.empty_like(df1)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = numpy.empty_like(df1)\n",
    "testPredictPlot[:, :] = numpy.nan\n",
    "testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(df1))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "441"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_input=test_data[341:].reshape(1,-1)\n",
    "x_input.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_input=list(x_input)\n",
    "temp_input=temp_input[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.855322211872301,\n",
       " 0.8846218985519521,\n",
       " 0.8845372173765771,\n",
       " 0.8819121009399611,\n",
       " 0.8858921161825726,\n",
       " 0.9008802608180202,\n",
       " 0.9262426962486241,\n",
       " 0.935726987890592,\n",
       " 0.954356846473029,\n",
       " 0.956473875857397,\n",
       " 0.947709374206114,\n",
       " 0.948556185959861,\n",
       " 0.9614277246168177,\n",
       " 0.9626132610720637,\n",
       " 0.9667202980777374,\n",
       " 0.9648996528071809,\n",
       " 0.9808620543653146,\n",
       " 0.9319163349987298,\n",
       " 0.9599881446354475,\n",
       " 1.0000000000000002,\n",
       " 0.9840799390295536,\n",
       " 0.9781099161656364,\n",
       " 0.9391989160809553,\n",
       " 0.9652383775086799,\n",
       " 0.9869167584046066,\n",
       " 0.9888644254382253,\n",
       " 0.9811584384791261,\n",
       " 0.9733254297569651,\n",
       " 0.9832754678634938,\n",
       " 0.9973325429756967,\n",
       " 0.993098484206961,\n",
       " 0.9920823101024643,\n",
       " 0.9657041239732409,\n",
       " 0.9861122872385468,\n",
       " 0.9864510119400456,\n",
       " 0.9686679651113557,\n",
       " 0.8997798289440258,\n",
       " 0.8927936319756118,\n",
       " 0.8731052587009909,\n",
       " 0.8228046405284106,\n",
       " 0.7906681344737065,\n",
       " 0.88817850791769,\n",
       " 0.8990176983656533,\n",
       " 0.8964772631044118,\n",
       " 0.8801761368447796,\n",
       " 0.8432128037937168,\n",
       " 0.789313235667711,\n",
       " 0.8246676263866544,\n",
       " 0.802565839613854,\n",
       " 0.7550597002286392,\n",
       " 0.7970615632144975,\n",
       " 0.7088237784740452,\n",
       " 0.7025997120840037,\n",
       " 0.6703785248539249,\n",
       " 0.6824032517571343,\n",
       " 0.6781268524007114,\n",
       " 0.5793449911084766,\n",
       " 0.6605978490981455,\n",
       " 0.7053095096959947,\n",
       " 0.707130154966551,\n",
       " 0.6952324498264038,\n",
       " 0.6937505292573463,\n",
       " 0.7232619188754341,\n",
       " 0.6649589296299432,\n",
       " 0.6498433398255568,\n",
       " 0.6521720721483615,\n",
       " 0.7258870353120501,\n",
       " 0.7622576001354899,\n",
       " 0.7439241256668643,\n",
       " 0.7553560843424507,\n",
       " 0.7707257176729614,\n",
       " 0.8323312727580658,\n",
       " 0.8242018799220934,\n",
       " 0.8321089846727072,\n",
       " 0.8268058260648659,\n",
       " 0.8045135066474722,\n",
       " 0.7857566263019731,\n",
       " 0.7885087645016513,\n",
       " 0.8048098907612837,\n",
       " 0.8101448048098907,\n",
       " 0.8166229147260564,\n",
       " 0.8220848505377254,\n",
       " 0.8383436362096708,\n",
       " 0.858921161825726,\n",
       " 0.8778474045219749,\n",
       " 0.8553645524599882,\n",
       " 0.8863155220594463,\n",
       " 0.8957998137014143,\n",
       " 0.9039715471250742,\n",
       " 0.9259039715471251,\n",
       " 0.9542721652976545,\n",
       " 0.965441612329579,\n",
       " 0.949614700652045,\n",
       " 0.9235328986366331,\n",
       " 0.9155305275637224,\n",
       " 0.9519434329748497,\n",
       " 0.9604962316876957,\n",
       " 0.9647302904564314,\n",
       " 0.9705309509695994,\n",
       " 0.9635024134134984]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.94820285]\n",
      "101\n",
      "1 day input [0.8846219  0.88453722 0.8819121  0.88589212 0.90088026 0.9262427\n",
      " 0.93572699 0.95435685 0.95647388 0.94770937 0.94855619 0.96142772\n",
      " 0.96261326 0.9667203  0.96489965 0.98086205 0.93191633 0.95998814\n",
      " 1.         0.98407994 0.97810992 0.93919892 0.96523838 0.98691676\n",
      " 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848\n",
      " 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983\n",
      " 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177\n",
      " 0.89647726 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584\n",
      " 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852 0.68240325\n",
      " 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245\n",
      " 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704\n",
      " 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127 0.82420188\n",
      " 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989\n",
      " 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116 0.8778474\n",
      " 0.85536455 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217\n",
      " 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623\n",
      " 0.96473029 0.97053095 0.96350241 0.94820285]\n",
      "1 day output [[0.9425146]]\n",
      "2 day input [0.88453722 0.8819121  0.88589212 0.90088026 0.9262427  0.93572699\n",
      " 0.95435685 0.95647388 0.94770937 0.94855619 0.96142772 0.96261326\n",
      " 0.9667203  0.96489965 0.98086205 0.93191633 0.95998814 1.\n",
      " 0.98407994 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443\n",
      " 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231\n",
      " 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363\n",
      " 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726\n",
      " 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597\n",
      " 0.79706156 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685\n",
      " 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053\n",
      " 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576\n",
      " 0.74392413 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898\n",
      " 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448\n",
      " 0.81662291 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455\n",
      " 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161\n",
      " 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623 0.96473029\n",
      " 0.97053095 0.96350241 0.94820285 0.9425146 ]\n",
      "2 day output [[0.9312872]]\n",
      "3 day input [0.8819121  0.88589212 0.90088026 0.9262427  0.93572699 0.95435685\n",
      " 0.95647388 0.94770937 0.94855619 0.96142772 0.96261326 0.9667203\n",
      " 0.96489965 0.98086205 0.93191633 0.95998814 1.         0.98407994\n",
      " 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844\n",
      " 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412\n",
      " 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526\n",
      " 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614\n",
      " 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597  0.79706156\n",
      " 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499\n",
      " 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192\n",
      " 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413\n",
      " 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583\n",
      " 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291\n",
      " 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552\n",
      " 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147\n",
      " 0.9235329  0.91553053 0.95194343 0.96049623 0.96473029 0.97053095\n",
      " 0.96350241 0.94820285 0.9425146  0.93128723]\n",
      "3 day output [[0.9197731]]\n",
      "4 day input [0.88589212 0.90088026 0.9262427  0.93572699 0.95435685 0.95647388\n",
      " 0.94770937 0.94855619 0.96142772 0.96261326 0.9667203  0.96489965\n",
      " 0.98086205 0.93191633 0.95998814 1.         0.98407994 0.97810992\n",
      " 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543\n",
      " 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229\n",
      " 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464\n",
      " 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128\n",
      " 0.78931324 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378\n",
      " 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785\n",
      " 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893\n",
      " 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608\n",
      " 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351\n",
      " 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485\n",
      " 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981\n",
      " 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329\n",
      " 0.91553053 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241\n",
      " 0.94820285 0.9425146  0.93128723 0.9197731 ]\n",
      "4 day output [[0.9105139]]\n",
      "5 day input [0.90088026 0.9262427  0.93572699 0.95435685 0.95647388 0.94770937\n",
      " 0.94855619 0.96142772 0.96261326 0.9667203  0.96489965 0.98086205\n",
      " 0.93191633 0.95998814 1.         0.98407994 0.97810992 0.93919892\n",
      " 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547\n",
      " 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101\n",
      " 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813\n",
      " 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324\n",
      " 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971\n",
      " 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951\n",
      " 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334\n",
      " 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572\n",
      " 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663\n",
      " 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364\n",
      " 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155\n",
      " 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053\n",
      " 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285\n",
      " 0.9425146  0.93128723 0.9197731  0.91051388]\n",
      "5 day output [[0.90409064]]\n",
      "6 day input [0.9262427  0.93572699 0.95435685 0.95647388 0.94770937 0.94855619\n",
      " 0.96142772 0.96261326 0.9667203  0.96489965 0.98086205 0.93191633\n",
      " 0.95998814 1.         0.98407994 0.97810992 0.93919892 0.96523838\n",
      " 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254\n",
      " 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797\n",
      " 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851\n",
      " 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324 0.82466763\n",
      " 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852\n",
      " 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015\n",
      " 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207\n",
      " 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127\n",
      " 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876\n",
      " 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116\n",
      " 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155 0.92590397\n",
      " 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343\n",
      " 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146\n",
      " 0.93128723 0.9197731  0.91051388 0.90409064]\n",
      "6 day output [[0.90018964]]\n",
      "7 day input [0.93572699 0.95435685 0.95647388 0.94770937 0.94855619 0.96142772\n",
      " 0.96261326 0.9667203  0.96489965 0.98086205 0.93191633 0.95998814\n",
      " 1.         0.98407994 0.97810992 0.93919892 0.96523838 0.98691676\n",
      " 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848\n",
      " 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983\n",
      " 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177\n",
      " 0.89647726 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584\n",
      " 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852 0.68240325\n",
      " 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245\n",
      " 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704\n",
      " 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127 0.82420188\n",
      " 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989\n",
      " 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116 0.8778474\n",
      " 0.85536455 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217\n",
      " 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623\n",
      " 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723\n",
      " 0.9197731  0.91051388 0.90409064 0.90018964]\n",
      "7 day output [[0.8982082]]\n",
      "8 day input [0.95435685 0.95647388 0.94770937 0.94855619 0.96142772 0.96261326\n",
      " 0.9667203  0.96489965 0.98086205 0.93191633 0.95998814 1.\n",
      " 0.98407994 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443\n",
      " 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231\n",
      " 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363\n",
      " 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726\n",
      " 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597\n",
      " 0.79706156 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685\n",
      " 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053\n",
      " 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576\n",
      " 0.74392413 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898\n",
      " 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448\n",
      " 0.81662291 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455\n",
      " 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161\n",
      " 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623 0.96473029\n",
      " 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731\n",
      " 0.91051388 0.90409064 0.90018964 0.8982082 ]\n",
      "8 day output [[0.89749014]]\n",
      "9 day input [0.95647388 0.94770937 0.94855619 0.96142772 0.96261326 0.9667203\n",
      " 0.96489965 0.98086205 0.93191633 0.95998814 1.         0.98407994\n",
      " 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844\n",
      " 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412\n",
      " 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526\n",
      " 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614\n",
      " 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597  0.79706156\n",
      " 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499\n",
      " 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192\n",
      " 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413\n",
      " 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583\n",
      " 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291\n",
      " 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552\n",
      " 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147\n",
      " 0.9235329  0.91553053 0.95194343 0.96049623 0.96473029 0.97053095\n",
      " 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388\n",
      " 0.90409064 0.90018964 0.8982082  0.89749014]\n",
      "9 day output [[0.89741695]]\n",
      "10 day input [0.94770937 0.94855619 0.96142772 0.96261326 0.9667203  0.96489965\n",
      " 0.98086205 0.93191633 0.95998814 1.         0.98407994 0.97810992\n",
      " 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543\n",
      " 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229\n",
      " 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464\n",
      " 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128\n",
      " 0.78931324 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378\n",
      " 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785\n",
      " 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893\n",
      " 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608\n",
      " 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351\n",
      " 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485\n",
      " 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981\n",
      " 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329\n",
      " 0.91553053 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241\n",
      " 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064\n",
      " 0.90018964 0.8982082  0.89749014 0.89741695]\n",
      "10 day output [[0.8974686]]\n",
      "11 day input [0.94855619 0.96142772 0.96261326 0.9667203  0.96489965 0.98086205\n",
      " 0.93191633 0.95998814 1.         0.98407994 0.97810992 0.93919892\n",
      " 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547\n",
      " 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101\n",
      " 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813\n",
      " 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324\n",
      " 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971\n",
      " 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951\n",
      " 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334\n",
      " 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572\n",
      " 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663\n",
      " 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364\n",
      " 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155\n",
      " 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053\n",
      " 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285\n",
      " 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064 0.90018964\n",
      " 0.8982082  0.89749014 0.89741695 0.89746863]\n",
      "11 day output [[0.89727265]]\n",
      "12 day input [0.96142772 0.96261326 0.9667203  0.96489965 0.98086205 0.93191633\n",
      " 0.95998814 1.         0.98407994 0.97810992 0.93919892 0.96523838\n",
      " 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254\n",
      " 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797\n",
      " 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851\n",
      " 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324 0.82466763\n",
      " 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852\n",
      " 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015\n",
      " 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207\n",
      " 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127\n",
      " 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876\n",
      " 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116\n",
      " 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155 0.92590397\n",
      " 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343\n",
      " 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146\n",
      " 0.93128723 0.9197731  0.91051388 0.90409064 0.90018964 0.8982082\n",
      " 0.89749014 0.89741695 0.89746863 0.89727265]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 day output [[0.8966242]]\n",
      "13 day input [0.96261326 0.9667203  0.96489965 0.98086205 0.93191633 0.95998814\n",
      " 1.         0.98407994 0.97810992 0.93919892 0.96523838 0.98691676\n",
      " 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848\n",
      " 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983\n",
      " 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177\n",
      " 0.89647726 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584\n",
      " 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852 0.68240325\n",
      " 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245\n",
      " 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704\n",
      " 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127 0.82420188\n",
      " 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989\n",
      " 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116 0.8778474\n",
      " 0.85536455 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217\n",
      " 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623\n",
      " 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723\n",
      " 0.9197731  0.91051388 0.90409064 0.90018964 0.8982082  0.89749014\n",
      " 0.89741695 0.89746863 0.89727265 0.89662421]\n",
      "13 day output [[0.89547694]]\n",
      "14 day input [0.9667203  0.96489965 0.98086205 0.93191633 0.95998814 1.\n",
      " 0.98407994 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443\n",
      " 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231\n",
      " 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363\n",
      " 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726\n",
      " 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597\n",
      " 0.79706156 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685\n",
      " 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053\n",
      " 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576\n",
      " 0.74392413 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898\n",
      " 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448\n",
      " 0.81662291 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455\n",
      " 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161\n",
      " 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623 0.96473029\n",
      " 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731\n",
      " 0.91051388 0.90409064 0.90018964 0.8982082  0.89749014 0.89741695\n",
      " 0.89746863 0.89727265 0.89662421 0.89547694]\n",
      "14 day output [[0.8939045]]\n",
      "15 day input [0.96489965 0.98086205 0.93191633 0.95998814 1.         0.98407994\n",
      " 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844\n",
      " 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412\n",
      " 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526\n",
      " 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614\n",
      " 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597  0.79706156\n",
      " 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499\n",
      " 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192\n",
      " 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413\n",
      " 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583\n",
      " 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291\n",
      " 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552\n",
      " 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147\n",
      " 0.9235329  0.91553053 0.95194343 0.96049623 0.96473029 0.97053095\n",
      " 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388\n",
      " 0.90409064 0.90018964 0.8982082  0.89749014 0.89741695 0.89746863\n",
      " 0.89727265 0.89662421 0.89547694 0.89390451]\n",
      "15 day output [[0.89205223]]\n",
      "16 day input [0.98086205 0.93191633 0.95998814 1.         0.98407994 0.97810992\n",
      " 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543\n",
      " 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229\n",
      " 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464\n",
      " 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128\n",
      " 0.78931324 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378\n",
      " 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785\n",
      " 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893\n",
      " 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608\n",
      " 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351\n",
      " 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485\n",
      " 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981\n",
      " 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329\n",
      " 0.91553053 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241\n",
      " 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064\n",
      " 0.90018964 0.8982082  0.89749014 0.89741695 0.89746863 0.89727265\n",
      " 0.89662421 0.89547694 0.89390451 0.89205223]\n",
      "16 day output [[0.8900886]]\n",
      "17 day input [0.93191633 0.95998814 1.         0.98407994 0.97810992 0.93919892\n",
      " 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547\n",
      " 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101\n",
      " 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813\n",
      " 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324\n",
      " 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971\n",
      " 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951\n",
      " 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334\n",
      " 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572\n",
      " 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663\n",
      " 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364\n",
      " 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155\n",
      " 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053\n",
      " 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285\n",
      " 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064 0.90018964\n",
      " 0.8982082  0.89749014 0.89741695 0.89746863 0.89727265 0.89662421\n",
      " 0.89547694 0.89390451 0.89205223 0.89008862]\n",
      "17 day output [[0.8881677]]\n",
      "18 day input [0.95998814 1.         0.98407994 0.97810992 0.93919892 0.96523838\n",
      " 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254\n",
      " 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797\n",
      " 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851\n",
      " 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324 0.82466763\n",
      " 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852\n",
      " 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015\n",
      " 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207\n",
      " 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127\n",
      " 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876\n",
      " 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116\n",
      " 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155 0.92590397\n",
      " 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343\n",
      " 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146\n",
      " 0.93128723 0.9197731  0.91051388 0.90409064 0.90018964 0.8982082\n",
      " 0.89749014 0.89741695 0.89746863 0.89727265 0.89662421 0.89547694\n",
      " 0.89390451 0.89205223 0.89008862 0.88816768]\n",
      "18 day output [[0.8864044]]\n",
      "19 day input [1.         0.98407994 0.97810992 0.93919892 0.96523838 0.98691676\n",
      " 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848\n",
      " 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983\n",
      " 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177\n",
      " 0.89647726 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584\n",
      " 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852 0.68240325\n",
      " 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245\n",
      " 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704\n",
      " 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127 0.82420188\n",
      " 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989\n",
      " 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116 0.8778474\n",
      " 0.85536455 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217\n",
      " 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623\n",
      " 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723\n",
      " 0.9197731  0.91051388 0.90409064 0.90018964 0.8982082  0.89749014\n",
      " 0.89741695 0.89746863 0.89727265 0.89662421 0.89547694 0.89390451\n",
      " 0.89205223 0.89008862 0.88816768 0.8864044 ]\n",
      "19 day output [[0.8848638]]\n",
      "20 day input [0.98407994 0.97810992 0.93919892 0.96523838 0.98691676 0.98886443\n",
      " 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231\n",
      " 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363\n",
      " 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726\n",
      " 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597\n",
      " 0.79706156 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685\n",
      " 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053\n",
      " 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576\n",
      " 0.74392413 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898\n",
      " 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448\n",
      " 0.81662291 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455\n",
      " 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161\n",
      " 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623 0.96473029\n",
      " 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731\n",
      " 0.91051388 0.90409064 0.90018964 0.8982082  0.89749014 0.89741695\n",
      " 0.89746863 0.89727265 0.89662421 0.89547694 0.89390451 0.89205223\n",
      " 0.89008862 0.88816768 0.8864044  0.88486379]\n",
      "20 day output [[0.8835602]]\n",
      "21 day input [0.97810992 0.93919892 0.96523838 0.98691676 0.98886443 0.98115844\n",
      " 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412\n",
      " 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526\n",
      " 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614\n",
      " 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597  0.79706156\n",
      " 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499\n",
      " 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192\n",
      " 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413\n",
      " 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583\n",
      " 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291\n",
      " 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552\n",
      " 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147\n",
      " 0.9235329  0.91553053 0.95194343 0.96049623 0.96473029 0.97053095\n",
      " 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388\n",
      " 0.90409064 0.90018964 0.8982082  0.89749014 0.89741695 0.89746863\n",
      " 0.89727265 0.89662421 0.89547694 0.89390451 0.89205223 0.89008862\n",
      " 0.88816768 0.8864044  0.88486379 0.88356018]\n",
      "21 day output [[0.8824673]]\n",
      "22 day input [0.93919892 0.96523838 0.98691676 0.98886443 0.98115844 0.97332543\n",
      " 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229\n",
      " 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464\n",
      " 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128\n",
      " 0.78931324 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378\n",
      " 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785\n",
      " 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893\n",
      " 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608\n",
      " 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351\n",
      " 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485\n",
      " 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981\n",
      " 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329\n",
      " 0.91553053 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241\n",
      " 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064\n",
      " 0.90018964 0.8982082  0.89749014 0.89741695 0.89746863 0.89727265\n",
      " 0.89662421 0.89547694 0.89390451 0.89205223 0.89008862 0.88816768\n",
      " 0.8864044  0.88486379 0.88356018 0.88246733]\n",
      "22 day output [[0.88153034]]\n",
      "23 day input [0.96523838 0.98691676 0.98886443 0.98115844 0.97332543 0.98327547\n",
      " 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101\n",
      " 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813\n",
      " 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324\n",
      " 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971\n",
      " 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951\n",
      " 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334\n",
      " 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572\n",
      " 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663\n",
      " 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364\n",
      " 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155\n",
      " 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053\n",
      " 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285\n",
      " 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064 0.90018964\n",
      " 0.8982082  0.89749014 0.89741695 0.89746863 0.89727265 0.89662421\n",
      " 0.89547694 0.89390451 0.89205223 0.89008862 0.88816768 0.8864044\n",
      " 0.88486379 0.88356018 0.88246733 0.88153034]\n",
      "23 day output [[0.8806824]]\n",
      "24 day input [0.98691676 0.98886443 0.98115844 0.97332543 0.98327547 0.99733254\n",
      " 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797\n",
      " 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851\n",
      " 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324 0.82466763\n",
      " 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852\n",
      " 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015\n",
      " 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207\n",
      " 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127\n",
      " 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876\n",
      " 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116\n",
      " 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155 0.92590397\n",
      " 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343\n",
      " 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146\n",
      " 0.93128723 0.9197731  0.91051388 0.90409064 0.90018964 0.8982082\n",
      " 0.89749014 0.89741695 0.89746863 0.89727265 0.89662421 0.89547694\n",
      " 0.89390451 0.89205223 0.89008862 0.88816768 0.8864044  0.88486379\n",
      " 0.88356018 0.88246733 0.88153034 0.88068241]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 day output [[0.87985766]]\n",
      "25 day input [0.98886443 0.98115844 0.97332543 0.98327547 0.99733254 0.99309848\n",
      " 0.99208231 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983\n",
      " 0.89279363 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177\n",
      " 0.89647726 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584\n",
      " 0.7550597  0.79706156 0.70882378 0.70259971 0.67037852 0.68240325\n",
      " 0.67812685 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245\n",
      " 0.69375053 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704\n",
      " 0.7622576  0.74392413 0.75535608 0.77072572 0.83233127 0.82420188\n",
      " 0.83210898 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989\n",
      " 0.8101448  0.81662291 0.82208485 0.83834364 0.85892116 0.8778474\n",
      " 0.85536455 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217\n",
      " 0.96544161 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623\n",
      " 0.96473029 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723\n",
      " 0.9197731  0.91051388 0.90409064 0.90018964 0.8982082  0.89749014\n",
      " 0.89741695 0.89746863 0.89727265 0.89662421 0.89547694 0.89390451\n",
      " 0.89205223 0.89008862 0.88816768 0.8864044  0.88486379 0.88356018\n",
      " 0.88246733 0.88153034 0.88068241 0.87985766]\n",
      "25 day output [[0.8790029]]\n",
      "26 day input [0.98115844 0.97332543 0.98327547 0.99733254 0.99309848 0.99208231\n",
      " 0.96570412 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363\n",
      " 0.87310526 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726\n",
      " 0.88017614 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597\n",
      " 0.79706156 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685\n",
      " 0.57934499 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053\n",
      " 0.72326192 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576\n",
      " 0.74392413 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898\n",
      " 0.82680583 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448\n",
      " 0.81662291 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455\n",
      " 0.88631552 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161\n",
      " 0.9496147  0.9235329  0.91553053 0.95194343 0.96049623 0.96473029\n",
      " 0.97053095 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731\n",
      " 0.91051388 0.90409064 0.90018964 0.8982082  0.89749014 0.89741695\n",
      " 0.89746863 0.89727265 0.89662421 0.89547694 0.89390451 0.89205223\n",
      " 0.89008862 0.88816768 0.8864044  0.88486379 0.88356018 0.88246733\n",
      " 0.88153034 0.88068241 0.87985766 0.87900293]\n",
      "26 day output [[0.8780826]]\n",
      "27 day input [0.97332543 0.98327547 0.99733254 0.99309848 0.99208231 0.96570412\n",
      " 0.98611229 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526\n",
      " 0.82280464 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614\n",
      " 0.8432128  0.78931324 0.82466763 0.80256584 0.7550597  0.79706156\n",
      " 0.70882378 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499\n",
      " 0.66059785 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192\n",
      " 0.66495893 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413\n",
      " 0.75535608 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583\n",
      " 0.80451351 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291\n",
      " 0.82208485 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552\n",
      " 0.89579981 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147\n",
      " 0.9235329  0.91553053 0.95194343 0.96049623 0.96473029 0.97053095\n",
      " 0.96350241 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388\n",
      " 0.90409064 0.90018964 0.8982082  0.89749014 0.89741695 0.89746863\n",
      " 0.89727265 0.89662421 0.89547694 0.89390451 0.89205223 0.89008862\n",
      " 0.88816768 0.8864044  0.88486379 0.88356018 0.88246733 0.88153034\n",
      " 0.88068241 0.87985766 0.87900293 0.87808257]\n",
      "27 day output [[0.87708074]]\n",
      "28 day input [0.98327547 0.99733254 0.99309848 0.99208231 0.96570412 0.98611229\n",
      " 0.98645101 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464\n",
      " 0.79066813 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128\n",
      " 0.78931324 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378\n",
      " 0.70259971 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785\n",
      " 0.70530951 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893\n",
      " 0.64984334 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608\n",
      " 0.77072572 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351\n",
      " 0.78575663 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485\n",
      " 0.83834364 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981\n",
      " 0.90397155 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329\n",
      " 0.91553053 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241\n",
      " 0.94820285 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064\n",
      " 0.90018964 0.8982082  0.89749014 0.89741695 0.89746863 0.89727265\n",
      " 0.89662421 0.89547694 0.89390451 0.89205223 0.89008862 0.88816768\n",
      " 0.8864044  0.88486379 0.88356018 0.88246733 0.88153034 0.88068241\n",
      " 0.87985766 0.87900293 0.87808257 0.87708074]\n",
      "28 day output [[0.8759997]]\n",
      "29 day input [0.99733254 0.99309848 0.99208231 0.96570412 0.98611229 0.98645101\n",
      " 0.96866797 0.89977983 0.89279363 0.87310526 0.82280464 0.79066813\n",
      " 0.88817851 0.8990177  0.89647726 0.88017614 0.8432128  0.78931324\n",
      " 0.82466763 0.80256584 0.7550597  0.79706156 0.70882378 0.70259971\n",
      " 0.67037852 0.68240325 0.67812685 0.57934499 0.66059785 0.70530951\n",
      " 0.70713015 0.69523245 0.69375053 0.72326192 0.66495893 0.64984334\n",
      " 0.65217207 0.72588704 0.7622576  0.74392413 0.75535608 0.77072572\n",
      " 0.83233127 0.82420188 0.83210898 0.82680583 0.80451351 0.78575663\n",
      " 0.78850876 0.80480989 0.8101448  0.81662291 0.82208485 0.83834364\n",
      " 0.85892116 0.8778474  0.85536455 0.88631552 0.89579981 0.90397155\n",
      " 0.92590397 0.95427217 0.96544161 0.9496147  0.9235329  0.91553053\n",
      " 0.95194343 0.96049623 0.96473029 0.97053095 0.96350241 0.94820285\n",
      " 0.9425146  0.93128723 0.9197731  0.91051388 0.90409064 0.90018964\n",
      " 0.8982082  0.89749014 0.89741695 0.89746863 0.89727265 0.89662421\n",
      " 0.89547694 0.89390451 0.89205223 0.89008862 0.88816768 0.8864044\n",
      " 0.88486379 0.88356018 0.88246733 0.88153034 0.88068241 0.87985766\n",
      " 0.87900293 0.87808257 0.87708074 0.87599969]\n",
      "29 day output [[0.87485355]]\n",
      "[[0.9482028484344482], [0.9425145983695984], [0.9312872290611267], [0.9197731018066406], [0.9105138778686523], [0.9040906429290771], [0.9001896381378174], [0.8982082009315491], [0.8974901437759399], [0.8974169492721558], [0.897468626499176], [0.8972726464271545], [0.8966242074966431], [0.8954769372940063], [0.8939045071601868], [0.8920522332191467], [0.8900886178016663], [0.888167679309845], [0.8864043951034546], [0.8848637938499451], [0.8835601806640625], [0.8824673295021057], [0.8815303444862366], [0.8806824088096619], [0.8798576593399048], [0.8790029287338257], [0.8780825734138489], [0.8770807385444641], [0.8759996891021729], [0.8748535513877869]]\n"
     ]
    }
   ],
   "source": [
    "# demonstrate prediction for next 30 days\n",
    "from numpy import array\n",
    "\n",
    "lst_output=[]\n",
    "n_steps=100\n",
    "i=0\n",
    "while(i<30):\n",
    "    \n",
    "    if(len(temp_input)>100):\n",
    "        #print(temp_input)\n",
    "        x_input=np.array(temp_input[1:])\n",
    "        print(\"{} day input {}\".format(i,x_input))\n",
    "        x_input=x_input.reshape(1,-1)\n",
    "        x_input = x_input.reshape((1, n_steps, 1))\n",
    "        #print(x_input)\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(\"{} day output {}\".format(i,yhat))\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        temp_input=temp_input[1:]\n",
    "        #print(temp_input)\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    else:\n",
    "        x_input = x_input.reshape((1, n_steps,1))\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(yhat[0])\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        print(len(temp_input))\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    \n",
    "\n",
    "print(lst_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_new=np.arange(1,101)\n",
    "day_pred=np.arange(101,131)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1258"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[315.61654874],\n",
       "       [314.27309784],\n",
       "       [311.62141776],\n",
       "       [308.90201118],\n",
       "       [306.71516768],\n",
       "       [305.19812805],\n",
       "       [304.27678874],\n",
       "       [303.8088129 ],\n",
       "       [303.63922216],\n",
       "       [303.62193508],\n",
       "       [303.63414021],\n",
       "       [303.58785363],\n",
       "       [303.43470533],\n",
       "       [303.16374305],\n",
       "       [302.7923665 ],\n",
       "       [302.35489644],\n",
       "       [301.89112975],\n",
       "       [301.4374425 ],\n",
       "       [301.02099004],\n",
       "       [300.65713083],\n",
       "       [300.34924347],\n",
       "       [300.09113388],\n",
       "       [299.86983676],\n",
       "       [299.66957131],\n",
       "       [299.47478198],\n",
       "       [299.27291171],\n",
       "       [299.05554219],\n",
       "       [298.81892883],\n",
       "       [298.56360657],\n",
       "       [298.29291177]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.inverse_transform(lst_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25cd7e8e670>]"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.plot(day_new,scaler.inverse_transform(df1[1158:]))\n",
    "plt.plot(day_pred,scaler.inverse_transform(lst_output))\n",
    "plt.savefig('30daypredict.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25cd7dcb610>]"
      ]
     },
     "execution_count": 42,
     "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": [
    "df3=df1.tolist()\n",
    "df3.extend(lst_output)\n",
    "plt.plot(df3[1200:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=scaler.inverse_transform(df3).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25cd7f281f0>]"
      ]
     },
     "execution_count": 47,
     "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": [
    "plt.plot(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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