Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Market Prediction using LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Keras and Tensorflow >2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Data Collection\n",
    "import pandas_datareader as pdr\n",
    "key=\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pdr.get_data_tiingo('AAPL', api_key=key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('AAPL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('AAPL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2019-05-27 00:00:00+00:00</td>\n",
       "      <td>132.045</td>\n",
       "      <td>132.260</td>\n",
       "      <td>130.05</td>\n",
       "      <td>130.34</td>\n",
       "      <td>45833246</td>\n",
       "      <td>121.682558</td>\n",
       "      <td>121.880685</td>\n",
       "      <td>119.844118</td>\n",
       "      <td>120.111360</td>\n",
       "      <td>45833246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2019-05-28 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",
       "      <td>30733309</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2019-05-29 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",
       "      <td>121.134251</td>\n",
       "      <td>119.705890</td>\n",
       "      <td>120.931516</td>\n",
       "      <td>50884452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2019-06-01 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>2019-06-02 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",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "   Unnamed: 0 symbol                       date    close     high     low  \\\n",
       "0           0   AAPL  2019-05-27 00:00:00+00:00  132.045  132.260  130.05   \n",
       "1           1   AAPL  2019-05-28 00:00:00+00:00  131.780  131.950  131.10   \n",
       "2           2   AAPL  2019-05-29 00:00:00+00:00  130.280  131.450  129.90   \n",
       "3           3   AAPL  2019-06-01 00:00:00+00:00  130.535  131.390  130.05   \n",
       "4           4   AAPL  2019-06-02 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",
       "3   32112797      0.0          1.0  \n",
       "4   33667627      0.0          1.0  "
      ]
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     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>adjLow</th>\n",
       "      <th>adjOpen</th>\n",
       "      <th>adjVolume</th>\n",
       "      <th>divCash</th>\n",
       "      <th>splitFactor</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>1253</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-05-18 00:00:00+00:00</td>\n",
       "      <td>314.96</td>\n",
       "      <td>316.50</td>\n",
       "      <td>310.3241</td>\n",
       "      <td>313.17</td>\n",
       "      <td>33843125</td>\n",
       "      <td>314.96</td>\n",
       "      <td>316.50</td>\n",
       "      <td>310.3241</td>\n",
       "      <td>313.17</td>\n",
       "      <td>33843125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1254</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-05-19 00:00:00+00:00</td>\n",
       "      <td>313.14</td>\n",
       "      <td>318.52</td>\n",
       "      <td>313.0100</td>\n",
       "      <td>315.03</td>\n",
       "      <td>25432385</td>\n",
       "      <td>313.14</td>\n",
       "      <td>318.52</td>\n",
       "      <td>313.0100</td>\n",
       "      <td>315.03</td>\n",
       "      <td>25432385</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1255</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-05-20 00:00:00+00:00</td>\n",
       "      <td>319.23</td>\n",
       "      <td>319.52</td>\n",
       "      <td>316.2000</td>\n",
       "      <td>316.68</td>\n",
       "      <td>27876215</td>\n",
       "      <td>319.23</td>\n",
       "      <td>319.52</td>\n",
       "      <td>316.2000</td>\n",
       "      <td>316.68</td>\n",
       "      <td>27876215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>1256</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-05-21 00:00:00+00:00</td>\n",
       "      <td>316.85</td>\n",
       "      <td>320.89</td>\n",
       "      <td>315.8700</td>\n",
       "      <td>318.66</td>\n",
       "      <td>25672211</td>\n",
       "      <td>316.85</td>\n",
       "      <td>320.89</td>\n",
       "      <td>315.8700</td>\n",
       "      <td>318.66</td>\n",
       "      <td>25672211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>1257</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>2024-05-22 00:00:00+00:00</td>\n",
       "      <td>318.89</td>\n",
       "      <td>319.23</td>\n",
       "      <td>315.3500</td>\n",
       "      <td>315.77</td>\n",
       "      <td>20450754</td>\n",
       "      <td>318.89</td>\n",
       "      <td>319.23</td>\n",
       "      <td>315.3500</td>\n",
       "      <td>315.77</td>\n",
       "      <td>20450754</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      Unnamed: 0 symbol                       date   close    high       low  \\\n",
       "1253        1253   AAPL  2024-05-18 00:00:00+00:00  314.96  316.50  310.3241   \n",
       "1254        1254   AAPL  2024-05-19 00:00:00+00:00  313.14  318.52  313.0100   \n",
       "1255        1255   AAPL  2024-05-20 00:00:00+00:00  319.23  319.52  316.2000   \n",
       "1256        1256   AAPL  2024-05-21 00:00:00+00:00  316.85  320.89  315.8700   \n",
       "1257        1257   AAPL  2024-05-22 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  "
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.reset_index()['close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       132.045\n",
       "1       131.780\n",
       "2       130.280\n",
       "3       130.535\n",
       "4       129.960\n",
       "         ...   \n",
       "1253    314.960\n",
       "1254    313.140\n",
       "1255    319.230\n",
       "1256    316.850\n",
       "1257    318.890\n",
       "Name: close, Length: 1258, dtype: float64"
      ]
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     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d1a92724e0>]"
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     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "### LSTM are sensitive to the scale of the data. so we apply MinMax scaler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       132.045\n",
       "1       131.780\n",
       "2       130.280\n",
       "3       130.535\n",
       "4       129.960\n",
       "         ...   \n",
       "1253    314.960\n",
       "1254    313.140\n",
       "1255    319.230\n",
       "1256    316.850\n",
       "1257    318.890\n",
       "Name: close, Length: 1258, dtype: float64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "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": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.17607447]\n",
      " [0.17495567]\n",
      " [0.16862282]\n",
      " ...\n",
      " [0.96635143]\n",
      " [0.9563033 ]\n",
      " [0.96491598]]\n"
     ]
    }
   ],
   "source": [
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "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": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(817, 441)"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_size,test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.17607447],\n",
       "       [0.17495567],\n",
       "       [0.16862282],\n",
       "       [0.1696994 ],\n",
       "       [0.16727181],\n",
       "       [0.16794731],\n",
       "       [0.16473866],\n",
       "       [0.16174111],\n",
       "       [0.1581525 ],\n",
       "       [0.15654817],\n",
       "       [0.16271215],\n",
       "       [0.1614878 ],\n",
       "       [0.1554927 ],\n",
       "       [0.15443722],\n",
       "       [0.15730811],\n",
       "       [0.15604154],\n",
       "       [0.15849025],\n",
       "       [0.15308621],\n",
       "       [0.15735033],\n",
       "       [0.15490163],\n",
       "       [0.15946129],\n",
       "       [0.15688592],\n",
       "       [0.1537195 ],\n",
       "       [0.14434687],\n",
       "       [0.14812547],\n",
       "       [0.15308621],\n",
       "       [0.15241071],\n",
       "       [0.15055307],\n",
       "       [0.14924428],\n",
       "       [0.13607194],\n",
       "       [0.12551718],\n",
       "       [0.13906949],\n",
       "       [0.14911762],\n",
       "       [0.14890653],\n",
       "       [0.15401503],\n",
       "       [0.16115005],\n",
       "       [0.16583636],\n",
       "       [0.17618002],\n",
       "       [0.17060711],\n",
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       "       [0.49299164],\n",
       "       [0.49358271],\n",
       "       [0.50046441],\n",
       "       [0.49476484],\n",
       "       [0.50042219],\n",
       "       [0.50413747],\n",
       "       [0.5062062 ],\n",
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       "       [0.52647133]])"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "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, 0,1,2,3-----99   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": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\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": 426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(716, 100)\n",
      "(716,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape), print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(340, 100)\n",
      "(340,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_test.shape), print(ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "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": 428,
   "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": 429,
   "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": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_7 (LSTM)                (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_8 (LSTM)                (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_9 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense_3 (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": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_4 (LSTM)                (None, 100, 50)           10400     \n",
      "_________________________________________________________________\n",
      "lstm_5 (LSTM)                (None, 100, 50)           20200     \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense_2 (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": 431,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "12/12 [==============================] - 6s 487ms/step - loss: 0.0206 - val_loss: 0.0505\n",
      "Epoch 2/100\n",
      "12/12 [==============================] - 4s 309ms/step - loss: 0.0035 - val_loss: 0.0046\n",
      "Epoch 3/100\n",
      "12/12 [==============================] - 4s 300ms/step - loss: 0.0014 - val_loss: 0.0040\n",
      "Epoch 4/100\n",
      "12/12 [==============================] - 3s 287ms/step - loss: 8.1361e-04 - val_loss: 0.0073\n",
      "Epoch 5/100\n",
      "12/12 [==============================] - 3s 290ms/step - loss: 6.6860e-04 - val_loss: 0.0062\n",
      "Epoch 6/100\n",
      "12/12 [==============================] - 3s 255ms/step - loss: 6.4653e-04 - val_loss: 0.0062\n",
      "Epoch 7/100\n",
      "12/12 [==============================] - 3s 291ms/step - loss: 6.6186e-04 - val_loss: 0.0062\n",
      "Epoch 8/100\n",
      "12/12 [==============================] - 4s 300ms/step - loss: 6.2498e-04 - val_loss: 0.0049\n",
      "Epoch 9/100\n",
      "12/12 [==============================] - 4s 297ms/step - loss: 6.2745e-04 - val_loss: 0.0042\n",
      "Epoch 10/100\n",
      "12/12 [==============================] - 4s 303ms/step - loss: 6.0206e-04 - val_loss: 0.0050\n",
      "Epoch 11/100\n",
      "12/12 [==============================] - 4s 298ms/step - loss: 5.9884e-04 - val_loss: 0.0061\n",
      "Epoch 12/100\n",
      "12/12 [==============================] - 4s 304ms/step - loss: 6.1458e-04 - val_loss: 0.0044\n",
      "Epoch 13/100\n",
      "12/12 [==============================] - 4s 304ms/step - loss: 5.6830e-04 - val_loss: 0.0041\n",
      "Epoch 14/100\n",
      "12/12 [==============================] - 3s 262ms/step - loss: 5.5734e-04 - val_loss: 0.0038\n",
      "Epoch 15/100\n",
      "12/12 [==============================] - 3s 244ms/step - loss: 5.5456e-04 - val_loss: 0.0034\n",
      "Epoch 16/100\n",
      "12/12 [==============================] - 3s 277ms/step - loss: 5.3865e-04 - val_loss: 0.0034\n",
      "Epoch 17/100\n",
      "12/12 [==============================] - 3s 271ms/step - loss: 5.3872e-04 - val_loss: 0.0032\n",
      "Epoch 18/100\n",
      "12/12 [==============================] - 3s 260ms/step - loss: 5.2315e-04 - val_loss: 0.0030\n",
      "Epoch 19/100\n",
      "12/12 [==============================] - 3s 275ms/step - loss: 5.1791e-04 - val_loss: 0.0029\n",
      "Epoch 20/100\n",
      "12/12 [==============================] - 3s 274ms/step - loss: 5.0077e-04 - val_loss: 0.0028\n",
      "Epoch 21/100\n",
      "12/12 [==============================] - 3s 273ms/step - loss: 4.8672e-04 - val_loss: 0.0032\n",
      "Epoch 22/100\n",
      "12/12 [==============================] - 3s 270ms/step - loss: 4.9148e-04 - val_loss: 0.0026\n",
      "Epoch 23/100\n",
      "12/12 [==============================] - 3s 283ms/step - loss: 4.9279e-04 - val_loss: 0.0026\n",
      "Epoch 24/100\n",
      "12/12 [==============================] - 4s 308ms/step - loss: 5.2013e-04 - val_loss: 0.0024\n",
      "Epoch 25/100\n",
      "12/12 [==============================] - 3s 275ms/step - loss: 5.7301e-04 - val_loss: 0.0024\n",
      "Epoch 26/100\n",
      "12/12 [==============================] - 4s 295ms/step - loss: 5.5014e-04 - val_loss: 0.0030\n",
      "Epoch 27/100\n",
      "12/12 [==============================] - 4s 301ms/step - loss: 4.8608e-04 - val_loss: 0.0022\n",
      "Epoch 28/100\n",
      "12/12 [==============================] - 3s 278ms/step - loss: 4.4525e-04 - val_loss: 0.0022\n",
      "Epoch 29/100\n",
      "12/12 [==============================] - 4s 299ms/step - loss: 4.2446e-04 - val_loss: 0.0028\n",
      "Epoch 30/100\n",
      "12/12 [==============================] - 4s 302ms/step - loss: 4.9896e-04 - val_loss: 0.0023\n",
      "Epoch 31/100\n",
      "12/12 [==============================] - 3s 278ms/step - loss: 4.7568e-04 - val_loss: 0.0022\n",
      "Epoch 32/100\n",
      "12/12 [==============================] - 4s 294ms/step - loss: 4.3184e-04 - val_loss: 0.0027\n",
      "Epoch 33/100\n",
      "12/12 [==============================] - 4s 292ms/step - loss: 4.1365e-04 - val_loss: 0.0025\n",
      "Epoch 34/100\n",
      "12/12 [==============================] - 3s 276ms/step - loss: 4.0967e-04 - val_loss: 0.0022\n",
      "Epoch 35/100\n",
      "12/12 [==============================] - 3s 250ms/step - loss: 3.9084e-04 - val_loss: 0.0018\n",
      "Epoch 36/100\n",
      "12/12 [==============================] - 3s 291ms/step - loss: 3.8744e-04 - val_loss: 0.0016\n",
      "Epoch 37/100\n",
      "12/12 [==============================] - 3s 254ms/step - loss: 3.6441e-04 - val_loss: 0.0024\n",
      "Epoch 38/100\n",
      "12/12 [==============================] - 3s 272ms/step - loss: 4.3088e-04 - val_loss: 0.0025\n",
      "Epoch 39/100\n",
      "12/12 [==============================] - 3s 259ms/step - loss: 4.1398e-04 - val_loss: 0.0016\n",
      "Epoch 40/100\n",
      "12/12 [==============================] - 3s 274ms/step - loss: 3.8981e-04 - val_loss: 0.0016\n",
      "Epoch 41/100\n",
      "12/12 [==============================] - 3s 261ms/step - loss: 3.4896e-04 - val_loss: 0.0028\n",
      "Epoch 42/100\n",
      "12/12 [==============================] - 3s 282ms/step - loss: 3.7910e-04 - val_loss: 0.0014\n",
      "Epoch 43/100\n",
      "12/12 [==============================] - 3s 274ms/step - loss: 3.6404e-04 - val_loss: 0.0022\n",
      "Epoch 44/100\n",
      "12/12 [==============================] - 3s 277ms/step - loss: 3.8073e-04 - val_loss: 0.0014\n",
      "Epoch 45/100\n",
      "12/12 [==============================] - 3s 276ms/step - loss: 4.0008e-04 - val_loss: 0.0016\n",
      "Epoch 46/100\n",
      "12/12 [==============================] - 3s 273ms/step - loss: 4.0253e-04 - val_loss: 0.0015\n",
      "Epoch 47/100\n",
      "12/12 [==============================] - 3s 286ms/step - loss: 3.5930e-04 - val_loss: 0.0018\n",
      "Epoch 48/100\n",
      "12/12 [==============================] - 3s 264ms/step - loss: 3.0690e-04 - val_loss: 0.0016\n",
      "Epoch 49/100\n",
      "12/12 [==============================] - 3s 288ms/step - loss: 3.0504e-04 - val_loss: 0.0022\n",
      "Epoch 50/100\n",
      "12/12 [==============================] - 3s 277ms/step - loss: 3.1205e-04 - val_loss: 0.0016\n",
      "Epoch 51/100\n",
      "12/12 [==============================] - 3s 291ms/step - loss: 2.8386e-04 - val_loss: 0.0014\n",
      "Epoch 52/100\n",
      "12/12 [==============================] - 3s 282ms/step - loss: 2.9832e-04 - val_loss: 0.0016\n",
      "Epoch 53/100\n",
      "12/12 [==============================] - 3s 287ms/step - loss: 2.8287e-04 - val_loss: 0.0018\n",
      "Epoch 54/100\n",
      "12/12 [==============================] - 3s 286ms/step - loss: 2.8193e-04 - val_loss: 0.0013\n",
      "Epoch 55/100\n",
      "12/12 [==============================] - 4s 295ms/step - loss: 2.8989e-04 - val_loss: 0.0026\n",
      "Epoch 56/100\n",
      "12/12 [==============================] - 3s 262ms/step - loss: 2.7761e-04 - val_loss: 0.0014\n",
      "Epoch 57/100\n",
      "12/12 [==============================] - 3s 270ms/step - loss: 2.6088e-04 - val_loss: 0.0016\n",
      "Epoch 58/100\n",
      "12/12 [==============================] - 3s 289ms/step - loss: 2.7300e-04 - val_loss: 0.0013\n",
      "Epoch 59/100\n",
      "12/12 [==============================] - 3s 288ms/step - loss: 2.6058e-04 - val_loss: 0.0020\n",
      "Epoch 60/100\n",
      "12/12 [==============================] - 3s 285ms/step - loss: 2.5682e-04 - val_loss: 0.0014\n",
      "Epoch 61/100\n",
      "12/12 [==============================] - 3s 285ms/step - loss: 2.4091e-04 - val_loss: 0.0013\n",
      "Epoch 62/100\n",
      "12/12 [==============================] - 4s 296ms/step - loss: 2.2724e-04 - val_loss: 0.0016\n",
      "Epoch 63/100\n",
      "12/12 [==============================] - 3s 258ms/step - loss: 2.3206e-04 - val_loss: 0.0012\n",
      "Epoch 64/100\n",
      "12/12 [==============================] - 3s 277ms/step - loss: 2.4468e-04 - val_loss: 0.0014\n",
      "Epoch 65/100\n",
      "12/12 [==============================] - 3s 266ms/step - loss: 2.2395e-04 - val_loss: 0.0012\n",
      "Epoch 66/100\n",
      "12/12 [==============================] - 3s 263ms/step - loss: 2.1142e-04 - val_loss: 0.0012\n",
      "Epoch 67/100\n",
      "12/12 [==============================] - 3s 281ms/step - loss: 2.0540e-04 - val_loss: 0.0016\n",
      "Epoch 68/100\n",
      "12/12 [==============================] - 4s 297ms/step - loss: 2.0560e-04 - val_loss: 0.0012\n",
      "Epoch 69/100\n",
      "12/12 [==============================] - 3s 218ms/step - loss: 1.9982e-04 - val_loss: 0.0014\n",
      "Epoch 70/100\n",
      "12/12 [==============================] - 3s 257ms/step - loss: 2.3622e-04 - val_loss: 0.0015\n",
      "Epoch 71/100\n",
      "12/12 [==============================] - 3s 283ms/step - loss: 2.6216e-04 - val_loss: 0.0012\n",
      "Epoch 72/100\n",
      "12/12 [==============================] - 3s 282ms/step - loss: 2.4869e-04 - val_loss: 0.0017\n",
      "Epoch 73/100\n",
      "12/12 [==============================] - 3s 280ms/step - loss: 2.1853e-04 - val_loss: 0.0013\n",
      "Epoch 74/100\n",
      "12/12 [==============================] - 3s 244ms/step - loss: 2.2121e-04 - val_loss: 0.0014\n",
      "Epoch 75/100\n",
      "12/12 [==============================] - 3s 283ms/step - loss: 1.9690e-04 - val_loss: 0.0011\n",
      "Epoch 76/100\n",
      "12/12 [==============================] - 3s 261ms/step - loss: 2.2144e-04 - val_loss: 0.0011\n",
      "Epoch 77/100\n",
      "12/12 [==============================] - 3s 282ms/step - loss: 1.8420e-04 - val_loss: 0.0011\n",
      "Epoch 78/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 3s 282ms/step - loss: 1.7841e-04 - val_loss: 0.0014\n",
      "Epoch 79/100\n",
      "12/12 [==============================] - 3s 260ms/step - loss: 1.9611e-04 - val_loss: 0.0013\n",
      "Epoch 80/100\n",
      "12/12 [==============================] - 3s 281ms/step - loss: 2.0224e-04 - val_loss: 0.0012\n",
      "Epoch 81/100\n",
      "12/12 [==============================] - 3s 290ms/step - loss: 2.1049e-04 - val_loss: 0.0020\n",
      "Epoch 82/100\n",
      "12/12 [==============================] - 3s 288ms/step - loss: 1.9466e-04 - val_loss: 0.0010\n",
      "Epoch 83/100\n",
      "12/12 [==============================] - 3s 284ms/step - loss: 1.5801e-04 - val_loss: 0.0010\n",
      "Epoch 84/100\n",
      "12/12 [==============================] - 3s 272ms/step - loss: 1.6260e-04 - val_loss: 9.4397e-04\n",
      "Epoch 85/100\n",
      "12/12 [==============================] - 3s 249ms/step - loss: 1.5695e-04 - val_loss: 0.0013\n",
      "Epoch 86/100\n",
      "12/12 [==============================] - 3s 242ms/step - loss: 2.0192e-04 - val_loss: 9.7445e-04\n",
      "Epoch 87/100\n",
      "12/12 [==============================] - 3s 271ms/step - loss: 2.2179e-04 - val_loss: 0.0020\n",
      "Epoch 88/100\n",
      "12/12 [==============================] - 3s 249ms/step - loss: 2.5509e-04 - val_loss: 0.0015\n",
      "Epoch 89/100\n",
      "12/12 [==============================] - 3s 261ms/step - loss: 1.9912e-04 - val_loss: 0.0011\n",
      "Epoch 90/100\n",
      "12/12 [==============================] - 3s 265ms/step - loss: 1.6930e-04 - val_loss: 8.9285e-04\n",
      "Epoch 91/100\n",
      "12/12 [==============================] - 3s 276ms/step - loss: 1.6435e-04 - val_loss: 9.1264e-04\n",
      "Epoch 92/100\n",
      "12/12 [==============================] - 3s 259ms/step - loss: 1.6799e-04 - val_loss: 0.0014\n",
      "Epoch 93/100\n",
      "12/12 [==============================] - 3s 282ms/step - loss: 1.9593e-04 - val_loss: 0.0016\n",
      "Epoch 94/100\n",
      "12/12 [==============================] - 3s 287ms/step - loss: 1.8104e-04 - val_loss: 0.0010\n",
      "Epoch 95/100\n",
      "12/12 [==============================] - 3s 277ms/step - loss: 1.3988e-04 - val_loss: 8.5343e-04\n",
      "Epoch 96/100\n",
      "12/12 [==============================] - 3s 280ms/step - loss: 1.4097e-04 - val_loss: 9.3255e-04\n",
      "Epoch 97/100\n",
      "12/12 [==============================] - 3s 287ms/step - loss: 1.4070e-04 - val_loss: 8.3848e-04\n",
      "Epoch 98/100\n",
      "12/12 [==============================] - 3s 290ms/step - loss: 1.3528e-04 - val_loss: 8.4349e-04\n",
      "Epoch 99/100\n",
      "12/12 [==============================] - 3s 288ms/step - loss: 1.4087e-04 - val_loss: 9.8092e-04\n",
      "Epoch 100/100\n",
      "12/12 [==============================] - 3s 285ms/step - loss: 1.4775e-04 - val_loss: 9.3230e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2d1aa544a58>"
      ]
     },
     "execution_count": 431,
     "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "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": 433,
   "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": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140.9909210035748"
      ]
     },
     "execution_count": 434,
     "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": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235.7193088627771"
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Test Data RMSE\n",
    "math.sqrt(mean_squared_error(ytest,test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "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": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "441"
      ]
     },
     "execution_count": 437,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 438,
     "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": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_input=list(x_input)\n",
    "temp_input=temp_input[0].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8583551465000423,\n",
       " 0.8866418981676942,\n",
       " 0.8743139407244789,\n",
       " 0.8843198513890065,\n",
       " 0.8783669678290975,\n",
       " 0.8986321033521913,\n",
       " 0.925821160179009,\n",
       " 0.9287764924427933,\n",
       " 0.9567677108840666,\n",
       " 0.9386979650426415,\n",
       " 0.933040614709111,\n",
       " 0.9495060373216249,\n",
       " 0.9642404796082076,\n",
       " 0.9551211686228154,\n",
       " 0.9598919192772104,\n",
       " 0.9663514312251966,\n",
       " 0.9624672802499368,\n",
       " 0.9229502659799038,\n",
       " 0.9598497002448705,\n",
       " 0.9879253567508233,\n",
       " 0.985941062230854,\n",
       " 0.9253145317909315,\n",
       " 0.9217259140420504,\n",
       " 0.964747107996285,\n",
       " 0.9757240564046274,\n",
       " 0.9915984125643842,\n",
       " 0.9697289538123788,\n",
       " 0.9761462467280253,\n",
       " 0.9679557544541082,\n",
       " 1.0000000000000002,\n",
       " 0.9901629654648318,\n",
       " 0.9905007177235499,\n",
       " 0.9653803934813816,\n",
       " 0.9848855864223593,\n",
       " 0.9708688676855528,\n",
       " 0.9402600692392133,\n",
       " 0.8774803681499621,\n",
       " 0.8348391454867856,\n",
       " 0.8541332432660644,\n",
       " 0.7733682344000676,\n",
       " 0.7726927298826314,\n",
       " 0.8801401671873683,\n",
       " 0.8400743054969182,\n",
       " 0.8967322468969012,\n",
       " 0.8552731571392387,\n",
       " 0.8388499535590646,\n",
       " 0.7423372456303303,\n",
       " 0.8232711306256861,\n",
       " 0.7814320695769654,\n",
       " 0.6665963016127672,\n",
       " 0.7921557037912694,\n",
       " 0.6411804441442204,\n",
       " 0.6861437135860848,\n",
       " 0.6600101325677616,\n",
       " 0.6520307354555435,\n",
       " 0.5864223591995272,\n",
       " 0.5658616904500551,\n",
       " 0.660896732246897,\n",
       " 0.6551549438486872,\n",
       " 0.7097019336316812,\n",
       " 0.664527569028118,\n",
       " 0.6943764248923416,\n",
       " 0.692181035210673,\n",
       " 0.6356919699400492,\n",
       " 0.6526640209406402,\n",
       " 0.637802921557038,\n",
       " 0.7267162036646122,\n",
       " 0.7138816178333194,\n",
       " 0.7419150553069325,\n",
       " 0.7500211095161702,\n",
       " 0.7722283205268936,\n",
       " 0.8304905851557884,\n",
       " 0.8194291986827664,\n",
       " 0.8289706999915563,\n",
       " 0.8125474964113824,\n",
       " 0.7877649244279323,\n",
       " 0.7516254327450818,\n",
       " 0.7842607447437306,\n",
       " 0.7797433082833742,\n",
       " 0.8132652199611587,\n",
       " 0.8141096006079542,\n",
       " 0.7947310647639958,\n",
       " 0.8333614793548934,\n",
       " 0.8589884319851391,\n",
       " 0.8390188296884238,\n",
       " 0.8562864139153934,\n",
       " 0.8748627881448958,\n",
       " 0.887824031073208,\n",
       " 0.9009541501308793,\n",
       " 0.9279321117959978,\n",
       " 0.9485349995778098,\n",
       " 0.9333361479354896,\n",
       " 0.9174617917757326,\n",
       " 0.925441188887951,\n",
       " 0.9177151059697712,\n",
       " 0.9483239044161109,\n",
       " 0.9406400405302711,\n",
       " 0.9663514312251966,\n",
       " 0.9563033015283293,\n",
       " 0.964915984125644]"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.94413203]\n",
      "101\n",
      "1 day input [0.8866419  0.87431394 0.88431985 0.87836697 0.8986321  0.92582116\n",
      " 0.92877649 0.95676771 0.93869797 0.93304061 0.94950604 0.96424048\n",
      " 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497\n",
      " 0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406\n",
      " 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297\n",
      " 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037\n",
      " 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431\n",
      " 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207\n",
      " 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074\n",
      " 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757\n",
      " 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162\n",
      " 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292\n",
      " 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331\n",
      " 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883\n",
      " 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535\n",
      " 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004\n",
      " 0.96635143 0.9563033  0.96491598 0.94413203]\n",
      "1 day output [[0.9379593]]\n",
      "2 day input [0.87431394 0.88431985 0.87836697 0.8986321  0.92582116 0.92877649\n",
      " 0.95676771 0.93869797 0.93304061 0.94950604 0.96424048 0.95512117\n",
      " 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536\n",
      " 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841\n",
      " 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072\n",
      " 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915\n",
      " 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225\n",
      " 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963\n",
      " 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236\n",
      " 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642\n",
      " 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162\n",
      " 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707\n",
      " 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522\n",
      " 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641\n",
      " 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615\n",
      " 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143\n",
      " 0.9563033  0.96491598 0.94413203 0.93795931]\n",
      "2 day output [[0.9286534]]\n",
      "3 day input [0.88431985 0.87836697 0.8986321  0.92582116 0.92877649 0.95676771\n",
      " 0.93869797 0.93304061 0.94950604 0.96424048 0.95512117 0.95989192\n",
      " 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106\n",
      " 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895\n",
      " 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039\n",
      " 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324\n",
      " 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316\n",
      " 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557\n",
      " 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169\n",
      " 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104\n",
      " 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506\n",
      " 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475\n",
      " 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096\n",
      " 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279\n",
      " 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179\n",
      " 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033\n",
      " 0.96491598 0.94413203 0.93795931 0.92865342]\n",
      "3 day output [[0.91987926]]\n",
      "4 day input [0.87836697 0.8986321  0.92582116 0.92877649 0.95676771 0.93869797\n",
      " 0.93304061 0.94950604 0.96424048 0.95512117 0.95989192 0.96635143\n",
      " 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453\n",
      " 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625\n",
      " 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559\n",
      " 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823\n",
      " 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995\n",
      " 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044\n",
      " 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673\n",
      " 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197\n",
      " 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111\n",
      " 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492\n",
      " 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106\n",
      " 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403\n",
      " 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119\n",
      " 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598\n",
      " 0.94413203 0.93795931 0.92865342 0.91987926]\n",
      "4 day output [[0.9128097]]\n",
      "5 day input [0.8986321  0.92582116 0.92877649 0.95676771 0.93869797 0.93304061\n",
      " 0.94950604 0.96424048 0.95512117 0.95989192 0.96635143 0.96246728\n",
      " 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591\n",
      " 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575\n",
      " 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887\n",
      " 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273\n",
      " 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725\n",
      " 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371\n",
      " 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494\n",
      " 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402\n",
      " 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832\n",
      " 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543\n",
      " 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148\n",
      " 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415\n",
      " 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511\n",
      " 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203\n",
      " 0.93795931 0.92865342 0.91987926 0.91280973]\n",
      "5 day output [[0.90777564]]\n",
      "6 day input [0.92582116 0.92877649 0.95676771 0.93869797 0.93304061 0.94950604\n",
      " 0.96424048 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027\n",
      " 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591 0.96474711\n",
      " 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.\n",
      " 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007\n",
      " 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017\n",
      " 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113\n",
      " 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013\n",
      " 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193\n",
      " 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292\n",
      " 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059\n",
      " 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074\n",
      " 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843\n",
      " 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211\n",
      " 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239\n",
      " 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931\n",
      " 0.92865342 0.91987926 0.91280973 0.90777564]\n",
      "6 day output [[0.9047326]]\n",
      "7 day input [0.92877649 0.95676771 0.93869797 0.93304061 0.94950604 0.96424048\n",
      " 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497\n",
      " 0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406\n",
      " 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297\n",
      " 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037\n",
      " 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431\n",
      " 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207\n",
      " 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074\n",
      " 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757\n",
      " 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162\n",
      " 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292\n",
      " 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331\n",
      " 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883\n",
      " 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535\n",
      " 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004\n",
      " 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342\n",
      " 0.91987926 0.91280973 0.90777564 0.90473258]\n",
      "7 day output [[0.9033923]]\n",
      "8 day input [0.95676771 0.93869797 0.93304061 0.94950604 0.96424048 0.95512117\n",
      " 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536\n",
      " 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841\n",
      " 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072\n",
      " 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915\n",
      " 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225\n",
      " 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963\n",
      " 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236\n",
      " 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642\n",
      " 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162\n",
      " 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707\n",
      " 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522\n",
      " 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641\n",
      " 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615\n",
      " 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143\n",
      " 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926\n",
      " 0.91280973 0.90777564 0.90473258 0.90339231]\n",
      "8 day output [[0.90332204]]\n",
      "9 day input [0.93869797 0.93304061 0.94950604 0.96424048 0.95512117 0.95989192\n",
      " 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106\n",
      " 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895\n",
      " 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039\n",
      " 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324\n",
      " 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316\n",
      " 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557\n",
      " 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169\n",
      " 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104\n",
      " 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506\n",
      " 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475\n",
      " 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096\n",
      " 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279\n",
      " 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179\n",
      " 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033\n",
      " 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973\n",
      " 0.90777564 0.90473258 0.90339231 0.90332204]\n",
      "9 day output [[0.9040391]]\n",
      "10 day input [0.93304061 0.94950604 0.96424048 0.95512117 0.95989192 0.96635143\n",
      " 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453\n",
      " 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625\n",
      " 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559\n",
      " 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823\n",
      " 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995\n",
      " 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044\n",
      " 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673\n",
      " 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197\n",
      " 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111\n",
      " 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492\n",
      " 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106\n",
      " 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403\n",
      " 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119\n",
      " 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598\n",
      " 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564\n",
      " 0.90473258 0.90339231 0.90332204 0.90403908]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 day output [[0.9050924]]\n",
      "11 day input [0.94950604 0.96424048 0.95512117 0.95989192 0.96635143 0.96246728\n",
      " 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591\n",
      " 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575\n",
      " 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887\n",
      " 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273\n",
      " 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725\n",
      " 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371\n",
      " 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494\n",
      " 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402\n",
      " 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832\n",
      " 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543\n",
      " 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148\n",
      " 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415\n",
      " 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511\n",
      " 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203\n",
      " 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258\n",
      " 0.90339231 0.90332204 0.90403908 0.90509242]\n",
      "11 day output [[0.906118]]\n",
      "12 day input [0.96424048 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027\n",
      " 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591 0.96474711\n",
      " 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.\n",
      " 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007\n",
      " 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017\n",
      " 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113\n",
      " 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013\n",
      " 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193\n",
      " 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292\n",
      " 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059\n",
      " 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074\n",
      " 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843\n",
      " 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211\n",
      " 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239\n",
      " 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931\n",
      " 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231\n",
      " 0.90332204 0.90403908 0.90509242 0.90611798]\n",
      "12 day output [[0.90686554]]\n",
      "13 day input [0.95512117 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497\n",
      " 0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406\n",
      " 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297\n",
      " 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037\n",
      " 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431\n",
      " 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207\n",
      " 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074\n",
      " 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757\n",
      " 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162\n",
      " 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292\n",
      " 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331\n",
      " 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883\n",
      " 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535\n",
      " 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004\n",
      " 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342\n",
      " 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204\n",
      " 0.90403908 0.90509242 0.90611798 0.90686554]\n",
      "13 day output [[0.90720606]]\n",
      "14 day input [0.95989192 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536\n",
      " 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841\n",
      " 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072\n",
      " 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915\n",
      " 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225\n",
      " 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963\n",
      " 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236\n",
      " 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642\n",
      " 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162\n",
      " 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707\n",
      " 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522\n",
      " 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641\n",
      " 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615\n",
      " 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143\n",
      " 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926\n",
      " 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908\n",
      " 0.90509242 0.90611798 0.90686554 0.90720606]\n",
      "14 day output [[0.9071163]]\n",
      "15 day input [0.96635143 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106\n",
      " 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895\n",
      " 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039\n",
      " 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324\n",
      " 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316\n",
      " 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557\n",
      " 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169\n",
      " 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104\n",
      " 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506\n",
      " 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475\n",
      " 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096\n",
      " 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279\n",
      " 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179\n",
      " 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033\n",
      " 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973\n",
      " 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242\n",
      " 0.90611798 0.90686554 0.90720606 0.90711629]\n",
      "15 day output [[0.9066538]]\n",
      "16 day input [0.96246728 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453\n",
      " 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625\n",
      " 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559\n",
      " 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823\n",
      " 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995\n",
      " 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044\n",
      " 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673\n",
      " 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197\n",
      " 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111\n",
      " 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492\n",
      " 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106\n",
      " 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403\n",
      " 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119\n",
      " 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598\n",
      " 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564\n",
      " 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798\n",
      " 0.90686554 0.90720606 0.90711629 0.90665382]\n",
      "16 day output [[0.90592706]]\n",
      "17 day input [0.92295027 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591\n",
      " 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575\n",
      " 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887\n",
      " 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273\n",
      " 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725\n",
      " 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371\n",
      " 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494\n",
      " 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402\n",
      " 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832\n",
      " 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543\n",
      " 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148\n",
      " 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415\n",
      " 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511\n",
      " 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203\n",
      " 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258\n",
      " 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554\n",
      " 0.90720606 0.90711629 0.90665382 0.90592706]\n",
      "17 day output [[0.9050646]]\n",
      "18 day input [0.9598497  0.98792536 0.98594106 0.92531453 0.92172591 0.96474711\n",
      " 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.\n",
      " 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007\n",
      " 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017\n",
      " 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113\n",
      " 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013\n",
      " 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193\n",
      " 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292\n",
      " 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059\n",
      " 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074\n",
      " 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843\n",
      " 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211\n",
      " 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239\n",
      " 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931\n",
      " 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231\n",
      " 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606\n",
      " 0.90711629 0.90665382 0.90592706 0.90506458]\n",
      "18 day output [[0.90419257]]\n",
      "19 day input [0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406\n",
      " 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297\n",
      " 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037\n",
      " 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431\n",
      " 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207\n",
      " 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074\n",
      " 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757\n",
      " 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162\n",
      " 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292\n",
      " 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331\n",
      " 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883\n",
      " 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535\n",
      " 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004\n",
      " 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342\n",
      " 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204\n",
      " 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629\n",
      " 0.90665382 0.90592706 0.90506458 0.90419257]\n",
      "19 day output [[0.9034131]]\n",
      "20 day input [0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841\n",
      " 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072\n",
      " 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915\n",
      " 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225\n",
      " 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963\n",
      " 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236\n",
      " 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642\n",
      " 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162\n",
      " 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707\n",
      " 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522\n",
      " 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641\n",
      " 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615\n",
      " 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143\n",
      " 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926\n",
      " 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908\n",
      " 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382\n",
      " 0.90592706 0.90506458 0.90419257 0.90341312]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 day output [[0.90279734]]\n",
      "21 day input [0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895\n",
      " 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039\n",
      " 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324\n",
      " 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316\n",
      " 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557\n",
      " 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169\n",
      " 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104\n",
      " 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506\n",
      " 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475\n",
      " 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096\n",
      " 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279\n",
      " 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179\n",
      " 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033\n",
      " 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973\n",
      " 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242\n",
      " 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706\n",
      " 0.90506458 0.90419257 0.90341312 0.90279734]\n",
      "21 day output [[0.9023812]]\n",
      "22 day input [0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625\n",
      " 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559\n",
      " 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823\n",
      " 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995\n",
      " 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044\n",
      " 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673\n",
      " 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197\n",
      " 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111\n",
      " 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492\n",
      " 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106\n",
      " 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403\n",
      " 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119\n",
      " 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598\n",
      " 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564\n",
      " 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798\n",
      " 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458\n",
      " 0.90419257 0.90341312 0.90279734 0.90238118]\n",
      "22 day output [[0.9021694]]\n",
      "23 day input [0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575\n",
      " 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887\n",
      " 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273\n",
      " 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725\n",
      " 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371\n",
      " 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494\n",
      " 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402\n",
      " 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832\n",
      " 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543\n",
      " 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148\n",
      " 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415\n",
      " 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511\n",
      " 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203\n",
      " 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258\n",
      " 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554\n",
      " 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458 0.90419257\n",
      " 0.90341312 0.90279734 0.90238118 0.90216941]\n",
      "23 day output [[0.90213937]]\n",
      "24 day input [0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.\n",
      " 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007\n",
      " 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017\n",
      " 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113\n",
      " 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013\n",
      " 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193\n",
      " 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292\n",
      " 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059\n",
      " 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074\n",
      " 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843\n",
      " 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211\n",
      " 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239\n",
      " 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931\n",
      " 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231\n",
      " 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606\n",
      " 0.90711629 0.90665382 0.90592706 0.90506458 0.90419257 0.90341312\n",
      " 0.90279734 0.90238118 0.90216941 0.90213937]\n",
      "24 day output [[0.9022528]]\n",
      "25 day input [0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297\n",
      " 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037\n",
      " 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431\n",
      " 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207\n",
      " 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074\n",
      " 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757\n",
      " 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162\n",
      " 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292\n",
      " 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331\n",
      " 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883\n",
      " 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535\n",
      " 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004\n",
      " 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342\n",
      " 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204\n",
      " 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629\n",
      " 0.90665382 0.90592706 0.90506458 0.90419257 0.90341312 0.90279734\n",
      " 0.90238118 0.90216941 0.90213937 0.90225279]\n",
      "25 day output [[0.90246403]]\n",
      "26 day input [0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072\n",
      " 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915\n",
      " 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225\n",
      " 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963\n",
      " 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236\n",
      " 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642\n",
      " 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162\n",
      " 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707\n",
      " 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522\n",
      " 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641\n",
      " 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615\n",
      " 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143\n",
      " 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926\n",
      " 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908\n",
      " 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382\n",
      " 0.90592706 0.90506458 0.90419257 0.90341312 0.90279734 0.90238118\n",
      " 0.90216941 0.90213937 0.90225279 0.90246403]\n",
      "26 day output [[0.90272856]]\n",
      "27 day input [0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039\n",
      " 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324\n",
      " 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316\n",
      " 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557\n",
      " 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169\n",
      " 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104\n",
      " 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506\n",
      " 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475\n",
      " 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096\n",
      " 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279\n",
      " 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179\n",
      " 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033\n",
      " 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973\n",
      " 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242\n",
      " 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706\n",
      " 0.90506458 0.90419257 0.90341312 0.90279734 0.90238118 0.90216941\n",
      " 0.90213937 0.90225279 0.90246403 0.90272856]\n",
      "27 day output [[0.90300757]]\n",
      "28 day input [0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559\n",
      " 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823\n",
      " 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995\n",
      " 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044\n",
      " 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673\n",
      " 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197\n",
      " 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111\n",
      " 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492\n",
      " 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106\n",
      " 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403\n",
      " 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119\n",
      " 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598\n",
      " 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564\n",
      " 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798\n",
      " 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458\n",
      " 0.90419257 0.90341312 0.90279734 0.90238118 0.90216941 0.90213937\n",
      " 0.90225279 0.90246403 0.90272856 0.90300757]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 day output [[0.903272]]\n",
      "29 day input [1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887\n",
      " 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273\n",
      " 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725\n",
      " 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371\n",
      " 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494\n",
      " 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402\n",
      " 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832\n",
      " 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543\n",
      " 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148\n",
      " 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415\n",
      " 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511\n",
      " 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203\n",
      " 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258\n",
      " 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554\n",
      " 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458 0.90419257\n",
      " 0.90341312 0.90279734 0.90238118 0.90216941 0.90213937 0.90225279\n",
      " 0.90246403 0.90272856 0.90300757 0.90327197]\n",
      "29 day output [[0.90350425]]\n",
      "[[0.9441320300102234], [0.9379593133926392], [0.9286534190177917], [0.9198792576789856], [0.9128097295761108], [0.9077756404876709], [0.9047325849533081], [0.9033923149108887], [0.9033220410346985], [0.9040390849113464], [0.9050924181938171], [0.9061179757118225], [0.9068655371665955], [0.9072060585021973], [0.9071162939071655], [0.9066538214683533], [0.9059270620346069], [0.905064582824707], [0.9041925668716431], [0.9034131169319153], [0.9027973413467407], [0.902381181716919], [0.902169406414032], [0.9021393656730652], [0.9022527933120728], [0.9024640321731567], [0.9027285575866699], [0.9030075669288635], [0.9032719731330872], [0.9035042524337769]]\n"
     ]
    }
   ],
   "source": [
    "# demonstrate prediction for next 10 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": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_new=np.arange(1,101)\n",
    "day_pred=np.arange(101,131)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1258"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d1b0f352b0>]"
      ]
     },
     "execution_count": 444,
     "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d1b0f55ac8>]"
      ]
     },
     "execution_count": 446,
     "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": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=scaler.inverse_transform(df3).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d1a904c470>]"
      ]
     },
     "execution_count": 396,
     "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": {},
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}