Time Series - Stock Price Forecast using ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "plt.style.use(\"ggplot\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (12, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download stock price data from NSE [website](https://www.nseindia.com/products/content/equities/equities/eq_security.htm). Here I download stock price data for TCS stock.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>31-May-2018</td>\n",
       "      <td>3514.10</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>1759.05</td>\n",
       "      <td>1726.10</td>\n",
       "      <td>1748.85</td>\n",
       "      <td>1741.05</td>\n",
       "      <td>1742.44</td>\n",
       "      <td>5049371</td>\n",
       "      <td>8.798250e+09</td>\n",
       "      <td>207998</td>\n",
       "      <td>3447026</td>\n",
       "      <td>68.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>01-Jun-2018</td>\n",
       "      <td>1741.05</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1757.50</td>\n",
       "      <td>1716.30</td>\n",
       "      <td>1732.00</td>\n",
       "      <td>1732.45</td>\n",
       "      <td>1742.29</td>\n",
       "      <td>1603856</td>\n",
       "      <td>2.794386e+09</td>\n",
       "      <td>74272</td>\n",
       "      <td>924751</td>\n",
       "      <td>57.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>04-Jun-2018</td>\n",
       "      <td>1732.45</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>1749.00</td>\n",
       "      <td>1712.60</td>\n",
       "      <td>1742.00</td>\n",
       "      <td>1744.25</td>\n",
       "      <td>1730.25</td>\n",
       "      <td>1681483</td>\n",
       "      <td>2.909394e+09</td>\n",
       "      <td>96686</td>\n",
       "      <td>928452</td>\n",
       "      <td>55.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>05-Jun-2018</td>\n",
       "      <td>1744.25</td>\n",
       "      <td>1744.0</td>\n",
       "      <td>1751.90</td>\n",
       "      <td>1711.15</td>\n",
       "      <td>1723.00</td>\n",
       "      <td>1721.60</td>\n",
       "      <td>1728.88</td>\n",
       "      <td>2449568</td>\n",
       "      <td>4.235001e+09</td>\n",
       "      <td>133067</td>\n",
       "      <td>1492228</td>\n",
       "      <td>60.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>06-Jun-2018</td>\n",
       "      <td>1721.60</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>1734.90</td>\n",
       "      <td>1715.20</td>\n",
       "      <td>1725.00</td>\n",
       "      <td>1725.75</td>\n",
       "      <td>1725.85</td>\n",
       "      <td>2236512</td>\n",
       "      <td>3.859875e+09</td>\n",
       "      <td>73770</td>\n",
       "      <td>1573892</td>\n",
       "      <td>70.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "0    TCS     EQ  31-May-2018     3514.10      1734.0     1759.05    1726.10   \n",
       "1    TCS     EQ  01-Jun-2018     1741.05      1754.0     1757.50    1716.30   \n",
       "2    TCS     EQ  04-Jun-2018     1732.45      1745.0     1749.00    1712.60   \n",
       "3    TCS     EQ  05-Jun-2018     1744.25      1744.0     1751.90    1711.15   \n",
       "4    TCS     EQ  06-Jun-2018     1721.60      1723.0     1734.90    1715.20   \n",
       "\n",
       "   Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "0     1748.85      1741.05        1742.44                5049371   \n",
       "1     1732.00      1732.45        1742.29                1603856   \n",
       "2     1742.00      1744.25        1730.25                1681483   \n",
       "3     1723.00      1721.60        1728.88                2449568   \n",
       "4     1725.00      1725.75        1725.85                2236512   \n",
       "\n",
       "       Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "0  8.798250e+09         207998          3447026                   68.27  \n",
       "1  2.794386e+09          74272           924751                   57.66  \n",
       "2  2.909394e+09          96686           928452                   55.22  \n",
       "3  4.235001e+09         133067          1492228                   60.92  \n",
       "4  3.859875e+09          73770          1573892                   70.37  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/abulbasar/data/master/tcs-stock.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check whether you have duplicate data on by date. It looks like 11/2/2019, 18/2/2019, 25/2/2019 etc. have duplicate data. Not sure why there are duplicates by date. It could be ammendment to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>TCS</td>\n",
       "      <td>BL</td>\n",
       "      <td>11-Feb-2019</td>\n",
       "      <td>2604.20</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>376308</td>\n",
       "      <td>7.757213e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>11-Feb-2019</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2060.00</td>\n",
       "      <td>2096.00</td>\n",
       "      <td>2048.90</td>\n",
       "      <td>2065.60</td>\n",
       "      <td>2065.90</td>\n",
       "      <td>2077.11</td>\n",
       "      <td>1778730</td>\n",
       "      <td>3.694622e+09</td>\n",
       "      <td>83603</td>\n",
       "      <td>901516</td>\n",
       "      <td>50.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>TCS</td>\n",
       "      <td>BL</td>\n",
       "      <td>18-Feb-2019</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2474600</td>\n",
       "      <td>5.022696e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-Feb-2019</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>2037.60</td>\n",
       "      <td>2041.95</td>\n",
       "      <td>1962.65</td>\n",
       "      <td>1969.60</td>\n",
       "      <td>1970.30</td>\n",
       "      <td>1988.87</td>\n",
       "      <td>2942184</td>\n",
       "      <td>5.851634e+09</td>\n",
       "      <td>100585</td>\n",
       "      <td>1668905</td>\n",
       "      <td>56.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>TCS</td>\n",
       "      <td>BL</td>\n",
       "      <td>25-Feb-2019</td>\n",
       "      <td>2029.70</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1057509</td>\n",
       "      <td>2.036392e+09</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>25-Feb-2019</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>1932.50</td>\n",
       "      <td>1990.00</td>\n",
       "      <td>1930.50</td>\n",
       "      <td>1987.00</td>\n",
       "      <td>1985.15</td>\n",
       "      <td>1961.61</td>\n",
       "      <td>2934880</td>\n",
       "      <td>5.757095e+09</td>\n",
       "      <td>140541</td>\n",
       "      <td>1341508</td>\n",
       "      <td>45.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>TCS</td>\n",
       "      <td>BL</td>\n",
       "      <td>22-Mar-2019</td>\n",
       "      <td>1925.65</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>264913</td>\n",
       "      <td>5.338129e+08</td>\n",
       "      <td>1</td>\n",
       "      <td>264913</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>204</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>22-Mar-2019</td>\n",
       "      <td>2015.05</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>1983.30</td>\n",
       "      <td>2010.00</td>\n",
       "      <td>2005.65</td>\n",
       "      <td>1998.96</td>\n",
       "      <td>3148149</td>\n",
       "      <td>6.293032e+09</td>\n",
       "      <td>155770</td>\n",
       "      <td>1787595</td>\n",
       "      <td>56.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Symbol Series         Date  Prev Close  Open Price  High Price  Low Price  \\\n",
       "173    TCS     BL  11-Feb-2019     2604.20     2061.40     2061.40    2061.40   \n",
       "174    TCS     EQ  11-Feb-2019     2061.40     2060.00     2096.00    2048.90   \n",
       "179    TCS     BL  18-Feb-2019     2061.40     2029.70     2029.70    2029.70   \n",
       "180    TCS     EQ  18-Feb-2019     2029.70     2037.60     2041.95    1962.65   \n",
       "185    TCS     BL  25-Feb-2019     2029.70     1925.65     1925.65    1925.65   \n",
       "186    TCS     EQ  25-Feb-2019     1925.65     1932.50     1990.00    1930.50   \n",
       "203    TCS     BL  22-Mar-2019     1925.65     2015.05     2015.05    2015.05   \n",
       "204    TCS     EQ  22-Mar-2019     2015.05     2015.00     2016.00    1983.30   \n",
       "\n",
       "     Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "173     2061.40      2061.40        2061.40                 376308   \n",
       "174     2065.60      2065.90        2077.11                1778730   \n",
       "179     2029.70      2029.70        2029.70                2474600   \n",
       "180     1969.60      1970.30        1988.87                2942184   \n",
       "185     1925.65      1925.65        1925.65                1057509   \n",
       "186     1987.00      1985.15        1961.61                2934880   \n",
       "203     2015.05      2015.05        2015.05                 264913   \n",
       "204     2010.00      2005.65        1998.96                3148149   \n",
       "\n",
       "         Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "173  7.757213e+08              1                0                    0.00  \n",
       "174  3.694622e+09          83603           901516                   50.68  \n",
       "179  5.022696e+09              1                0                    0.00  \n",
       "180  5.851634e+09         100585          1668905                   56.72  \n",
       "185  2.036392e+09              2                0                    0.00  \n",
       "186  5.757095e+09         140541          1341508                   45.71  \n",
       "203  5.338129e+08              1           264913                  100.00  \n",
       "204  6.293032e+09         155770          1787595                   56.78  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Date\"].duplicated(keep = False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the duplicate from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[~df[\"Date\"].duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the dataset into time series data by undating the index to time series index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Series</th>\n",
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       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>31-May-2018</td>\n",
       "      <td>3514.10</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>1759.05</td>\n",
       "      <td>1726.10</td>\n",
       "      <td>1748.85</td>\n",
       "      <td>1741.05</td>\n",
       "      <td>1742.44</td>\n",
       "      <td>5049371</td>\n",
       "      <td>8.798250e+09</td>\n",
       "      <td>207998</td>\n",
       "      <td>3447026</td>\n",
       "      <td>68.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>01-Jun-2018</td>\n",
       "      <td>1741.05</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>1757.50</td>\n",
       "      <td>1716.30</td>\n",
       "      <td>1732.00</td>\n",
       "      <td>1732.45</td>\n",
       "      <td>1742.29</td>\n",
       "      <td>1603856</td>\n",
       "      <td>2.794386e+09</td>\n",
       "      <td>74272</td>\n",
       "      <td>924751</td>\n",
       "      <td>57.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>04-Jun-2018</td>\n",
       "      <td>1732.45</td>\n",
       "      <td>1745.0</td>\n",
       "      <td>1749.00</td>\n",
       "      <td>1712.60</td>\n",
       "      <td>1742.00</td>\n",
       "      <td>1744.25</td>\n",
       "      <td>1730.25</td>\n",
       "      <td>1681483</td>\n",
       "      <td>2.909394e+09</td>\n",
       "      <td>96686</td>\n",
       "      <td>928452</td>\n",
       "      <td>55.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>05-Jun-2018</td>\n",
       "      <td>1744.25</td>\n",
       "      <td>1744.0</td>\n",
       "      <td>1751.90</td>\n",
       "      <td>1711.15</td>\n",
       "      <td>1723.00</td>\n",
       "      <td>1721.60</td>\n",
       "      <td>1728.88</td>\n",
       "      <td>2449568</td>\n",
       "      <td>4.235001e+09</td>\n",
       "      <td>133067</td>\n",
       "      <td>1492228</td>\n",
       "      <td>60.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>TCS</td>\n",
       "      <td>EQ</td>\n",
       "      <td>06-Jun-2018</td>\n",
       "      <td>1721.60</td>\n",
       "      <td>1723.0</td>\n",
       "      <td>1734.90</td>\n",
       "      <td>1715.20</td>\n",
       "      <td>1725.00</td>\n",
       "      <td>1725.75</td>\n",
       "      <td>1725.85</td>\n",
       "      <td>2236512</td>\n",
       "      <td>3.859875e+09</td>\n",
       "      <td>73770</td>\n",
       "      <td>1573892</td>\n",
       "      <td>70.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "Date                                                                        \n",
       "2018-05-31    TCS     EQ  31-May-2018     3514.10      1734.0     1759.05   \n",
       "2018-06-01    TCS     EQ  01-Jun-2018     1741.05      1754.0     1757.50   \n",
       "2018-06-04    TCS     EQ  04-Jun-2018     1732.45      1745.0     1749.00   \n",
       "2018-06-05    TCS     EQ  05-Jun-2018     1744.25      1744.0     1751.90   \n",
       "2018-06-06    TCS     EQ  06-Jun-2018     1721.60      1723.0     1734.90   \n",
       "\n",
       "            Low Price  Last Price  Close Price  Average Price  \\\n",
       "Date                                                            \n",
       "2018-05-31    1726.10     1748.85      1741.05        1742.44   \n",
       "2018-06-01    1716.30     1732.00      1732.45        1742.29   \n",
       "2018-06-04    1712.60     1742.00      1744.25        1730.25   \n",
       "2018-06-05    1711.15     1723.00      1721.60        1728.88   \n",
       "2018-06-06    1715.20     1725.00      1725.75        1725.85   \n",
       "\n",
       "            Total Traded Quantity      Turnover  No. of Trades  \\\n",
       "Date                                                             \n",
       "2018-05-31                5049371  8.798250e+09         207998   \n",
       "2018-06-01                1603856  2.794386e+09          74272   \n",
       "2018-06-04                1681483  2.909394e+09          96686   \n",
       "2018-06-05                2449568  4.235001e+09         133067   \n",
       "2018-06-06                2236512  3.859875e+09          73770   \n",
       "\n",
       "            Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "Date                                                 \n",
       "2018-05-31          3447026                   68.27  \n",
       "2018-06-01           924751                   57.66  \n",
       "2018-06-04           928452                   55.22  \n",
       "2018-06-05          1492228                   60.92  \n",
       "2018-06-06          1573892                   70.37  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.to_datetime(df[\"Date\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-05-31', '2018-06-01', '2018-06-04', '2018-06-05',\n",
       "               '2018-06-06', '2018-06-07', '2018-06-08', '2018-06-11',\n",
       "               '2018-06-12', '2018-06-13',\n",
       "               ...\n",
       "               '2019-05-17', '2019-05-20', '2019-05-21', '2019-05-22',\n",
       "               '2019-05-23', '2019-05-24', '2019-05-27', '2019-05-28',\n",
       "               '2019-05-29', '2019-05-30'],\n",
       "              dtype='datetime64[ns]', name='Date', length=246, freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f105ed0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = np.log(df[\"Close Price\"])\n",
    "y.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-05-31', '2018-06-01', '2018-06-04', '2018-06-05',\n",
       "               '2018-06-06', '2018-06-07', '2018-06-08', '2018-06-11',\n",
       "               '2018-06-12', '2018-06-13',\n",
       "               ...\n",
       "               '2019-05-17', '2019-05-20', '2019-05-21', '2019-05-22',\n",
       "               '2019-05-23', '2019-05-24', '2019-05-27', '2019-05-28',\n",
       "               '2019-05-29', '2019-05-30'],\n",
       "              dtype='datetime64[ns]', name='Date', length=246, freq=None)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if there are gaps in the data in terms of date range. Trading does not happen on weekends or national holidays. So there is no data on those days. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('2018-05-31 00:00:00'),\n",
       " Timestamp('2018-06-01 00:00:00'),\n",
       " Timestamp('2018-06-04 00:00:00'),\n",
       " Timestamp('2018-06-05 00:00:00'),\n",
       " Timestamp('2018-06-06 00:00:00'),\n",
       " Timestamp('2018-06-07 00:00:00'),\n",
       " Timestamp('2018-06-08 00:00:00'),\n",
       " Timestamp('2018-06-11 00:00:00'),\n",
       " Timestamp('2018-06-12 00:00:00'),\n",
       " Timestamp('2018-06-13 00:00:00')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(y.index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-05-31    7.462244\n",
       "2018-06-01    7.457292\n",
       "2018-06-02         NaN\n",
       "2018-06-03         NaN\n",
       "2018-06-04    7.464080\n",
       "2018-06-05    7.451009\n",
       "2018-06-06    7.453417\n",
       "2018-06-07    7.459080\n",
       "2018-06-08    7.465713\n",
       "2018-06-09         NaN\n",
       "2018-06-10         NaN\n",
       "2018-06-11    7.467200\n",
       "2018-06-12    7.484930\n",
       "2018-06-13    7.508842\n",
       "2018-06-14    7.488601\n",
       "Freq: D, Name: Close Price, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.asfreq(\"D\")[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's impute the missing data by forward fill using asfreq method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-05-31    7.462244\n",
       "2018-06-01    7.457292\n",
       "2018-06-02    7.457292\n",
       "2018-06-03    7.457292\n",
       "2018-06-04    7.464080\n",
       "2018-06-05    7.451009\n",
       "2018-06-06    7.453417\n",
       "2018-06-07    7.459080\n",
       "2018-06-08    7.465713\n",
       "2018-06-09    7.465713\n",
       "2018-06-10    7.465713\n",
       "2018-06-11    7.467200\n",
       "2018-06-12    7.484930\n",
       "2018-06-13    7.508842\n",
       "2018-06-14    7.488601\n",
       "Freq: D, Name: Close Price, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.asfreq(\"D\", method=\"ffill\")[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For legitimate gaps, we can also set frequency to business days (\"B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-05-31', '2018-06-01', '2018-06-04', '2018-06-05',\n",
       "               '2018-06-06', '2018-06-07', '2018-06-08', '2018-06-11',\n",
       "               '2018-06-12', '2018-06-13',\n",
       "               ...\n",
       "               '2019-05-17', '2019-05-20', '2019-05-21', '2019-05-22',\n",
       "               '2019-05-23', '2019-05-24', '2019-05-27', '2019-05-28',\n",
       "               '2019-05-29', '2019-05-30'],\n",
       "              dtype='datetime64[ns]', name='Date', length=261, freq='B')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.asfreq(\"B\")\n",
    "y.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate quarterly high, low etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>7.451009</td>\n",
       "      <td>7.527256</td>\n",
       "      <td>7.462244</td>\n",
       "      <td>7.521724</td>\n",
       "      <td>7.503576</td>\n",
       "      <td>7.491539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>7.523670</td>\n",
       "      <td>7.695508</td>\n",
       "      <td>7.523670</td>\n",
       "      <td>7.688776</td>\n",
       "      <td>7.602676</td>\n",
       "      <td>7.607829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>7.495042</td>\n",
       "      <td>7.721149</td>\n",
       "      <td>7.721149</td>\n",
       "      <td>7.545945</td>\n",
       "      <td>7.567837</td>\n",
       "      <td>7.572861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-03-31</td>\n",
       "      <td>7.502876</td>\n",
       "      <td>7.641516</td>\n",
       "      <td>7.551082</td>\n",
       "      <td>7.601727</td>\n",
       "      <td>7.593789</td>\n",
       "      <td>7.585512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>7.608126</td>\n",
       "      <td>7.723275</td>\n",
       "      <td>7.616604</td>\n",
       "      <td>7.671501</td>\n",
       "      <td>7.654088</td>\n",
       "      <td>7.655684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 low      high     first      last    median      mean\n",
       "Date                                                                  \n",
       "2018-06-30  7.451009  7.527256  7.462244  7.521724  7.503576  7.491539\n",
       "2018-09-30  7.523670  7.695508  7.523670  7.688776  7.602676  7.607829\n",
       "2018-12-31  7.495042  7.721149  7.721149  7.545945  7.567837  7.572861\n",
       "2019-03-31  7.502876  7.641516  7.551082  7.601727  7.593789  7.585512\n",
       "2019-06-30  7.608126  7.723275  7.616604  7.671501  7.654088  7.655684"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(dict(low=y.resample(\"Q\").min(), \n",
    "                  high=y.resample(\"Q\").max(), \n",
    "                  first = y.resample(\"Q\").first(), \n",
    "                  last = y.resample(\"Q\").last(),\n",
    "                  median = y.resample(\"Q\").median(),\n",
    "                  mean = y.resample(\"Q\").mean()\n",
    "                 ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate rolling mean, exponentially weighted mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>rolling_mean</th>\n",
       "      <th>ewm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>7.462244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.462244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>7.457292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.458942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>7.464080</td>\n",
       "      <td>7.461205</td>\n",
       "      <td>7.461878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>7.451009</td>\n",
       "      <td>7.457460</td>\n",
       "      <td>7.456081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>7.453417</td>\n",
       "      <td>7.456169</td>\n",
       "      <td>7.454706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>7.459080</td>\n",
       "      <td>7.454502</td>\n",
       "      <td>7.456928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>7.465713</td>\n",
       "      <td>7.459403</td>\n",
       "      <td>7.461355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>7.467200</td>\n",
       "      <td>7.463997</td>\n",
       "      <td>7.464289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-12</td>\n",
       "      <td>7.484930</td>\n",
       "      <td>7.472614</td>\n",
       "      <td>7.474630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>7.508842</td>\n",
       "      <td>7.486991</td>\n",
       "      <td>7.491753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              actual  rolling_mean       ewm\n",
       "Date                                        \n",
       "2018-05-31  7.462244           NaN  7.462244\n",
       "2018-06-01  7.457292           NaN  7.458942\n",
       "2018-06-04  7.464080      7.461205  7.461878\n",
       "2018-06-05  7.451009      7.457460  7.456081\n",
       "2018-06-06  7.453417      7.456169  7.454706\n",
       "2018-06-07  7.459080      7.454502  7.456928\n",
       "2018-06-08  7.465713      7.459403  7.461355\n",
       "2018-06-11  7.467200      7.463997  7.464289\n",
       "2018-06-12  7.484930      7.472614  7.474630\n",
       "2018-06-13  7.508842      7.486991  7.491753"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "period = 3\n",
    "y_rolling = pd.DataFrame(dict(actual = y, \n",
    "                              rolling_mean = y.rolling(period).mean(), \n",
    "                              ewm = y.ewm(span = period).mean()\n",
    "                             ))\n",
    "y_rolling.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rmse over rolling mean:  0.011226801116974697\n",
      "rmse over ewma:  0.008422829060485497\n"
     ]
    }
   ],
   "source": [
    "rolling_dropped = y_rolling.dropna()\n",
    "print(\"rmse over rolling mean: \", metrics.mean_squared_error(rolling_dropped.actual, rolling_dropped.rolling_mean) ** 0.5)\n",
    "print(\"rmse over ewma: \", metrics.mean_squared_error(rolling_dropped.actual, rolling_dropped[\"ewm\"]) ** 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By visual inspection, we see the time series is not stationary. Let we will see a more formal way of testing using Dickey Fullter Test. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108b92b50>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_rolling.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formal of way of test for stationarity. Since the p-value of Augmented Dickey Fullter test is > 0.05, we can say that there is not sufficient proof that the time series is stationary. Loosely speaking, it is non-stationary. We need further transformation to make data stationary. Populary method for making data stationary will be to take \"difference\". Taking difference by lag one, we see the p-val for Dickey Fuller test is < 0.05. So we conclude, diff 1 has made the data stationary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -2.645513\n",
      "p-value: 0.083951\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "from numpy import log\n",
    "result = adfuller(y.dropna())\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>diff1</th>\n",
       "      <th>diff2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-05-31</td>\n",
       "      <td>7.462244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-01</td>\n",
       "      <td>7.457292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>7.464080</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>7.451009</td>\n",
       "      <td>-0.011234</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-06</td>\n",
       "      <td>7.453417</td>\n",
       "      <td>-0.003875</td>\n",
       "      <td>0.007359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-07</td>\n",
       "      <td>7.459080</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>-0.001125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-08</td>\n",
       "      <td>7.465713</td>\n",
       "      <td>0.014703</td>\n",
       "      <td>0.019703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>7.467200</td>\n",
       "      <td>0.013783</td>\n",
       "      <td>-0.000921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-12</td>\n",
       "      <td>7.484930</td>\n",
       "      <td>0.025851</td>\n",
       "      <td>0.012068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-13</td>\n",
       "      <td>7.508842</td>\n",
       "      <td>0.043129</td>\n",
       "      <td>0.017279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              actual     diff1     diff2\n",
       "Date                                    \n",
       "2018-05-31  7.462244       NaN       NaN\n",
       "2018-06-01  7.457292       NaN       NaN\n",
       "2018-06-04  7.464080       NaN       NaN\n",
       "2018-06-05  7.451009 -0.011234       NaN\n",
       "2018-06-06  7.453417 -0.003875  0.007359\n",
       "2018-06-07  7.459080 -0.005000 -0.001125\n",
       "2018-06-08  7.465713  0.014703  0.019703\n",
       "2018-06-11  7.467200  0.013783 -0.000921\n",
       "2018-06-12  7.484930  0.025851  0.012068\n",
       "2018-06-13  7.508842  0.043129  0.017279"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = pd.DataFrame({\"actual\": y, \"diff1\": y.diff(3)}).head(10)\n",
    "diff[\"diff2\"] = diff.diff1.diff(1)\n",
    "diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -16.354529\n",
      "p-value: 0.000000\n"
     ]
    }
   ],
   "source": [
    "result = adfuller(y.diff(periods=1).dropna())\n",
    "print('ADF Statistic: %f' % result[0])\n",
    "print('p-value: %f' % result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f7fb390>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.diff(periods=1).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use pandas's auto correlation plot. Here is a nice explanation of the [autocorrelation plot](https://stats.stackexchange.com/questions/357300/what-does-pandas-autocorrelation-graph-show) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c237ec510>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A8ohYkpkr64adDzyRmb8fEWcDHwNeHxFHAmdTW8feDSyNiJmZubUdtUqSJEmtsN014lWgfRXwmzbWMQtYk5lrM/M5YDEwv2HMfGBR9fo64I8joqtqX5yZmzPzp9TWrc9qY62SJEnSqDW7Rvxy4B8i4kOZ+evtjt5x06gtd+m3DjhxqDGZuSUingKmVO13NZw7rZkPPeuss0ZaryRJkrRd3/ve94bsazaIvwM4ELgwIn5OtYUhQGZOH1V1NYOtm2lcvD7UmGbOBSAieoAegMzckfokSZKklmo2iL+xrVXUZrEPrjs+CGi8k7J/zLqImAzsBTze5LkAZOZCYGF12HfdddeNvnJJkiRpBJq9WfPPqd2sublNdSwHDo+IQ4H11G6+fEPDmCXAucAy4Czg1szsi4glwBcj4hPUbtY8HLi7TXVKkiRJLdERN2tm5hbgAuAmYFWtKR+IiEsj4sxq2NXAlIhYA1wIXFSd+wCQwErgRuDt7pgiSZKkTtfUPuIR8V5gb6BdN2uW4D7ikiRJaqvh9hFvNog/Su1mza1AO27WLMEgLkmSpLYa1QN9Ku2+WVOSJEmaUJp+xP045Iy4JEmS2mrUM+IRsQvwAeBN1HYm6QU+D1xWPQlTkiRJ0g5odmnKx6k9Nv6twMPAIcDfAy8A3t2e0iRJkqTxq9kg/jrgDzNzU3X844i4F/ghBnFJkiRph213H/HKoOtahmmXJEmSNIxmZ8S/BHwjIv4BeITa0pQPUHuQjiRJkqQd1GwQfy+14P1JajdrrgcWAx9pU12SJEnSuOb2hZIkSVKbDLd9YVNrxCPioog4oaFtVkS8d/TlSZIkSRNPszdrvgtY2dC2Evjr1pYjSZIkTQzNBvFdgV83tD0H7N7aciRJkqSJodkg/n3grxra3grc29pyJEmSpImh2V1T3g3cHBFvAh4Efh84AJjbrsLGwvXXX1+6BEmSJI1jPT09Q/Y1NSOemQ8AM4F/BJZTe+T9izKzcd24JEmSpCbs0PaFETEdmAasz8xH2lbV2HD7QkmSJLXVcNsXNhXEI2IqtQf4vAx4HJgC3AWcnZk7a5o1iEuSJKmtRr2POHAl8ENg38ycCuwD3Af8SysKlCRJkiaaZoP4ScB7MvNpgOrv9wJ/1K7CJEmSpPGs2V1TngCOpDYr3u9FwJMtr2gMNe6aMmPGDI488ki2bNnCjTfeOGD8zJkzmTlzJs8++yxLly4d0H/EEUdw2GGH8ctf/pLbb799QP9RRx3FIYccwpNPPsmdd945oP/YY49l2rRpbNq0iWXLlg3oP+GEEzjggAN47LHHWL58+YD+2bNnM2XKFNavX8999903oP+kk05i77335uGHH2bFihUD+ufMmcOee+7Jgw8+yKpVqwb0n3baaey+++6sXr2a1atXD+ifN28ekydPZuXKlaxdu3ZA/xlnnAHA/fffzyOPbHuLwaRJkzj99NMBuPfee2lcNrTbbrsxd25tk567776bjRs3btO/xx57cOqppwKwbNkyNm3atE3/XnvtxcknnwzAHXfcwVNPPbVN/5QpU5g9ezYAt912G08//fQ2/fvvvz+zZs0C4Oabb2bz5s3b9Hd3d3PccccBcMMNN7B169Zt+qdPn87RRx8NDL5bj9ee1x547Xntee3V89rz2oPxce0Nt2tKs0H848DSiLgaeBg4BHgL8PdNni9JkiSpTtO7pkTEK4E3AN1AL/DFzLy1jbW1mzdrSpIkqa2Gu1mzqRnxiHhdZn4JuLWh/azMvG7UFUqSJEkTTLM3a149RPvCVhUiSZIkTSTDzohHxIzq5fMi4lC2nVafATzbrsIkSZKk8Wx7S1PWAH3UAviDDX0/Ay5pQ02SJEnSuNfskzW/k5mnjEE9Y8mbNSVJktRWo37E/ThlEJckSVJbtWLXlDuoLVEZIDNfMeLKJEmSpAmq2Qf6XNVwfCBwPvBvrS1HkiRJmhhGvDQlIn4f+ExmntzaksaMS1MkSZLUVqNemjKE9cDRozgfgIjYF7gWeCHwEBCZ+UTDmGOAK4EXAFuByzLz2qrvs8ApwFPV8PMy8wejrUuSJElqp2bXiP95Q9PzgdcCd7WghouAWzJzQURcVB2/r2HMM8CbM/MnEdENfD8ibsrMJ6v+v/UJn5IkSdqZNDsj/qaG46eBfwcub0EN84E51etFwO00BPHMXF33ujciNgK/BzyJJEmStBNqKohn5qn1xxFxNPBm4EdA9yhrOCAzN1SfsyEi9h9ucETMAnZl2wcMXRYRHwRuAS7KzM1DnNsD9FSfNcqyJUmSpJFreo14RPwe8AbgXOAPgTuAdzV57lJqO600en+zn1+9z1Tg88C5mfmbqvliak/53BVYSG02/dLBzs/MhdUYGGI7RkmSJGksDBvEI2IX4EzgPODV1B55fw21GysjMzc28yGZedown/FYREytZsOnAoO+Z0S8APgm8IHM/O3a9P7ZdGBzRHwG+JtmapIkSZJKet52+h8D/hX4MfCyzDwyMz8MDLr0Y4SWUJtlp/r7640DImJX4KvA5zLzSw19U6u/u4DXUFsuI0mSJHW07QXx+4G9gROBEyJinzbUsACYGxE/AeZWx0TE8RHR/yChAF4BnBcRP6j+HFP1fSEiVgArgP2Aj7ShRkmSJKmltvtAn4g4hNqNmW8GpgPfprZv9xGZub7tFbaPD/SRJElSWw33QJ8derJmRJxELZAHsAX4dGa+twU1lmAQlyRJUlsNF8S3tzRlG5l5Z2b2UNsB5R3AUaOuTpIkSZqAdmhGfJxxRlySJElt1bIZcUmSJEmtYRCXJEmSCjCIS5IkSQUYxCVJkqQCDOKSJElSAQZxSZIkqQCDuCRJklSAQVySJEkqwCAuSZIkFWAQlyRJkgowiEuSJEkFGMQlSZKkAgzikiRJUgEGcUmSJKkAg7gkSZJUgEFckiRJKsAgLkmSJBVgEJckSZIKMIhLkiRJBRjEJUmSpAIM4pIkSVIBBnFJkiSpAIO4JEmSVIBBXJIkSSrAIC5JkiQVYBCXJEmSCjCIS5IkSQUYxCVJkqQCDOKSJElSAZNLFxAR+wLXAi8EHgIiM58YZNxWYEV1+Ehmnlm1HwosBvYF7gXelJnPtb9ySZIkaeQ6YUb8IuCWzDwcuKU6HsyvMvOY6s+Zde0fAy6vzn8COL+95UqSJEmj1wlBfD6wqHq9CHhNsydGRBfwSuC6kZwvSZIklVJ8aQpwQGZuAMjMDRGx/xDjdo+Ie4AtwILM/BowBXgyM7dUY9YB04b6oIjoAXqqz2pV/ZIkSdIOG5MgHhFLgQMH6Xr/DrzN9MzsjYgZwK0RsQL4xSDj+oZ6g8xcCCzc3jhJkiSp3br6+srm0Yj4MTCnmg2fCtyemS/azjmfBa4Hvgz8HDgwM7dExGzgksx8dRMf3dfb2zvK6iVJkqShdXd3A3QN1tcJa8SXAOdWr88Fvt44ICL2iYjdqtf7AS8HVmZmH3AbcNZw50uSJEmdphOC+AJgbkT8BJhbHRMRx0fEVdWYI4B7IuKH1IL3gsxcWfW9D7gwItZQWzN+9ZhWL0mSJI1A8aUpBbk0RZIkSW3V6UtTJEmSpAnHIC5JkiQVYBCXJEmSCjCIS5IkSQUYxCVJkqQCDOKSJElSAQZxSZIkqQCDuCRJklSAQVySJEkqwCAuSZIkFWAQlyRJkgowiEuSJEkFGMQlSZKkAgzikiRJUgEGcUmSJKkAg7gkSZJUgEFckiRJKsAgLkmSJBVgEJckSZIKMIhLkiRJBRjEJUmSpAIM4pIkSVIBBnFJkiSpAIO4JEmSVIBBXJIkSSrAIC5JkiQVYBCXJEmSCjCIS5IkSQUYxCVJkqQCDOKSJElSAQZxSZIkqQCDuCRJklTA5NIFRMS+wLXAC4GHgMjMJxrGnApcXtf0YuDszPxaRHwWOAV4quo7LzN/0OayJUmSpFEpHsSBi4BbMnNBRFxUHb+vfkBm3gYcA78N7muAb9cN+dvMvG6M6pUkSZJGrROWpswHFlWvFwGv2c74s4AbMvOZtlYlSZIktVEnzIgfkJkbADJzQ0Tsv53xZwOfaGi7LCI+CNwCXJSZmwc7MSJ6gJ7qs0ZXtSRJkjQKYxLEI2IpcOAgXe/fwfeZChwF3FTXfDHwM2BXYCG1ZS2XDnZ+Zi6sxgD07chnS5IkSa00JkE8M08bqi8iHouIqdVs+FRg4zBvFcBXM/PXde+9oXq5OSI+A/xNS4qWJEmS2qgT1ogvAc6tXp8LfH2YsecA19Q3VOGdiOiitr78R22oUZIkSWqpTgjiC4C5EfETYG51TEQcHxFX9Q+KiBcCBwPfaTj/CxGxAlgB7Ad8ZCyKliRJkkajq69vwi6V7uvt7S1dgyRJksax7u5ugK7B+jphRlySJEmacAzikiRJUgEGcUmSJKkAg7gkSZJUgEFckiRJKsAgLkmSJBVgEJckSZIKMIhLkiRJBRjEJUmSpAIM4pIkSVIBBnFJkiSpAIO4JEmSVIBBXJIkSSrAIC5JkiQVYBCXJEmSCjCIS5IkSQUYxCVJkqQCDOKSJElSAQZxSZIkqQCDuCRJklSAQVySJEkqwCAuSZIkFWAQlyRJkgowiEuSJEkFGMQlSZKkAgzikiRJUgEGcUmSJKkAg7gkSZJUgEFckiRJKsAgLkmSJBVgEJckSZIKmFy6gIh4HXAJcAQwKzPvGWLcPOAKYBJwVWYuqNoPBRYD+wL3Am/KzOfGoHRJkiRpxDphRvxHwGuB7w41ICImAZ8ETgeOBM6JiCOr7o8Bl2fm4cATwPntLVeSJEkaveJBPDNXZeaPtzNsFrAmM9dWs92LgfkR0QW8EriuGrcIeE37qpUkSZJao3gQb9I04NG643VV2xTgyczc0tAuSZIkdbQxWSMeEUuBAwfpen9mfr2Jt+gapK1vmPah6ugBegAyk+7u7iY+WpIkSWq9MQnimXnaKN9iHXBw3fFBQC/wX8DeETG5mhXvbx+qjoXAwlHWop1YRNyTmceXrkPleA3Ia2Bi8/tXJ10DO8vSlOXA4RFxaETsCpwNLMnMPuA24Kxq3LlAMzPskiRJUlHFg3hE/FlErANmA9+MiJuq9u6I+BZANdt9AXATsKrWlA9Ub/E+4MKIWENtzfjVY/2fQZIkSdpRxfcRz8yvAl8dpL0X+JO6428B3xpk3Fpqu6pIzXBpkrwG5DUwsfn9q2Ouga6+viHvbZQkSZLUJsWXpkiSJEkTUfGlKVI7RcRDwH8DW4EtmXl8ROwLXAu8EHgIiMx8olSNap2I+DRwBrAxM/+gahv0+64eCHYFtSVwzwDnZea9JepW6wxxDVwC/CXw82rY31XLHYmIi6k9kXkr8M7MvGnMi1ZLRcTBwOeobZv8G2BhZl7hb8HEMMz3fwkd+DvgjLgmglMz85i6rYouAm7JzMOBW6pjjQ+fBeY1tA31fZ8OHF796QGuHKMa1V6fZeA1AHB59TtwTN3/+B5JbReul1Tn/L+ImDRmlapdtgDvycwjgJcBb6++a38LJoahvn/owN8Bg7gmovnAour1IuA1BWtRC2Xmd4HHG5qH+r7nA5/LzL7MvIvaMwmmjk2lapchroGhzAcWZ+bmzPwpsAZv/t/pZeaG/hntzPxvarutTcPfgglhmO9/KEV/BwziGu/6gG9HxPerJ6sCHJCZG6D2X1hg/2LVaSwM9X1PAx6tG7eO4X+stXO7ICLuj4hPR8Q+VZvXwDgXES8EjgX+A38LJpyG7x868HfAIK7x7uWZeRy1f3p8e0S8onRB6hhdg7S5jdT4dCVwGHAMsAH4v1W718A4FhF7Al8G/jozfzHMUK+DcWiQ778jfwcM4hrXqv3oycyN1ParnwU81v/PjtXfG8tVqDEw1Pe9Dji4btxBQO8Y16YxkJmPZebWzPwN8Cn+55+dvQbGqYjYhVoI+0JmfqVq9rdgghjs++/U3wGDuMatiNgjIn63/zXwKuBHwBLg3GrYucDXy1SoMTLU970EeHNEdEXEy4Cn+v/ZWuNLw3rfP6P2OwC1a+DsiNgtIg6ldrPe3WNdn1qr2gXlamBVZn6irsvfgglgqO+/U38H3L5Q49kBwFcjAmrX+hcz88aIWA5kRJwPPAK8rmCNaqGIuAaYA+7tAfQAAAIoSURBVOwXEeuADwELGPz7/ha17crWUNuy7C1jXrBabohrYE5EHEPtn5sfAv4PQGY+EBEJrKS208LbM3NribrVUi8H3gSsiIgfVG1/h78FE8VQ3/85nfg74JM1JUmSpAJcmiJJkiQVYBCXJEmSCjCIS5IkSQUYxCVJkqQCDOKSJElSAQZxSZIkqQCDuCSJiHgoIk4rXYckTSQGcUmSJKkAn6wpSRpUROwDfB44kdr/Xvw78NbMXFf1HwosAo4F/gP4MbBXZr6xTMWStHNxRlySNJTnAZ8BDgGmA78C/rmu/4vA3cAU4BJqj5WWJDXJGXFJ0qAycxPw5f7jiLgMuK16PR04AfjjzHwOuDMilhQpVJJ2UgZxSdKgIuL5wOXAPGCfqvl3I2IS0A08npnP1J3yKHDw2FYpSTsvl6ZIkobyHuBFwImZ+QLgFVV7F7AB2LcK6/0M4ZK0A5wRlyT12yUidq873ofauvAnI2Jf4EP9HZn5cETcA1wSER8AXgr8KfCNsSxYknZmzohLkvp9i1rw7v+zN/A7wH8BdwE3Noz/38BsYBPwEeBaYPNYFStJO7uuvr6+0jVIksaBiLgW+M/M/NB2B0uSXJoiSRqZiDgBeBz4KfAqYD6woGhRkrQTMYhLkkbqQOAr1PYRXwe8LTPvK1uSJO08XJoiSZIkFeDNmpIkSVIBBnFJkiSpAIO4JEmSVIBBXJIkSSrAIC5JkiQVYBCXJEmSCvj/iP2ITbmJoF0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
      "/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                0\n",
      "count  245.000000\n",
      "mean    -0.000001\n",
      "std      0.015648\n",
      "min     -0.064712\n",
      "25%     -0.007517\n",
      "50%      0.000321\n",
      "75%      0.007957\n",
      "max      0.060259\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "# fit model\n",
    "model = ARIMA(y, order=(5,1,0)) # p, d, q\n",
    "model_fit = model.fit(disp=0)\n",
    "#print(model_fit.summary())\n",
    "\n",
    "# plot residual erros\n",
    "residuals = pd.DataFrame(model_fit.resid)\n",
    "residuals.plot()\n",
    "residuals.plot(kind='kde')\n",
    "print(residuals.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>D.Close Price</td>  <th>  No. Observations:  </th>    <td>245</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(5, 1, 0)</td>  <th>  Log Likelihood     </th>  <td>671.412</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.016</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Thu, 24 Oct 2019</td> <th>  AIC                </th> <td>-1328.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>15:49:50</td>     <th>  BIC                </th> <td>-1304.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                <td>1</td>        <th>  HQIC               </th> <td>-1318.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>               <td>    0.0008</td> <td>    0.001</td> <td>    0.922</td> <td> 0.357</td> <td>   -0.001</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Close Price</th> <td>   -0.0124</td> <td>    0.064</td> <td>   -0.193</td> <td> 0.847</td> <td>   -0.138</td> <td>    0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.D.Close Price</th> <td>   -0.0835</td> <td>    0.064</td> <td>   -1.307</td> <td> 0.193</td> <td>   -0.209</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.D.Close Price</th> <td>   -0.0153</td> <td>    0.064</td> <td>   -0.239</td> <td> 0.812</td> <td>   -0.141</td> <td>    0.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.D.Close Price</th> <td>   -0.0336</td> <td>    0.064</td> <td>   -0.526</td> <td> 0.599</td> <td>   -0.158</td> <td>    0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.D.Close Price</th> <td>    0.0545</td> <td>    0.064</td> <td>    0.857</td> <td> 0.393</td> <td>   -0.070</td> <td>    0.179</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          -1.3265</td> <td>          -1.1035j</td> <td>           1.7255</td> <td>          -0.3896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th> <td>          -1.3265</td> <td>          +1.1035j</td> <td>           1.7255</td> <td>           0.3896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th> <td>           0.5898</td> <td>          -1.6129j</td> <td>           1.7173</td> <td>          -0.1942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th> <td>           0.5898</td> <td>          +1.6129j</td> <td>           1.7173</td> <td>           0.1942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th> <td>           2.0889</td> <td>          -0.0000j</td> <td>           2.0889</td> <td>          -0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:          D.Close Price   No. Observations:                  245\n",
       "Model:                 ARIMA(5, 1, 0)   Log Likelihood                 671.412\n",
       "Method:                       css-mle   S.D. of innovations              0.016\n",
       "Date:                Thu, 24 Oct 2019   AIC                          -1328.824\n",
       "Time:                        15:49:50   BIC                          -1304.315\n",
       "Sample:                             1   HQIC                         -1318.954\n",
       "                                                                              \n",
       "=======================================================================================\n",
       "                          coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "const                   0.0008      0.001      0.922      0.357      -0.001       0.003\n",
       "ar.L1.D.Close Price    -0.0124      0.064     -0.193      0.847      -0.138       0.113\n",
       "ar.L2.D.Close Price    -0.0835      0.064     -1.307      0.193      -0.209       0.042\n",
       "ar.L3.D.Close Price    -0.0153      0.064     -0.239      0.812      -0.141       0.110\n",
       "ar.L4.D.Close Price    -0.0336      0.064     -0.526      0.599      -0.158       0.091\n",
       "ar.L5.D.Close Price     0.0545      0.064      0.857      0.393      -0.070       0.179\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           -1.3265           -1.1035j            1.7255           -0.3896\n",
       "AR.2           -1.3265           +1.1035j            1.7255            0.3896\n",
       "AR.3            0.5898           -1.6129j            1.7173           -0.1942\n",
       "AR.4            0.5898           +1.6129j            1.7173            0.1942\n",
       "AR.5            2.0889           -0.0000j            2.0889           -0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1c23e328d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = ARIMA(y.values, order=(5,1,0))\n",
    "model_fit = model.fit(disp=0)\n",
    "predictions, error, conf_interval = model_fit.forecast(150)\n",
    "plt.plot(predictions)\n",
    "plt.fill_between(range(len(predictions)), conf_interval[:,0], conf_interval[:,1], alpha = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.67043094, 7.66948429, 7.67017584, 7.67145335, 7.67334407,\n",
       "       7.67409748, 7.67475654, 7.67557198, 7.67642212, 7.67733182])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0156159 , 0.02194815, 0.02609877, 0.0295766 , 0.03251409,\n",
       "       0.03556232, 0.03839147, 0.04097907, 0.0434079 , 0.04569468])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.63982434, 7.70103754],\n",
       "       [7.62646671, 7.71250187],\n",
       "       [7.6190232 , 7.72132849],\n",
       "       [7.61348428, 7.72942242],\n",
       "       [7.60961762, 7.73707051],\n",
       "       [7.60439662, 7.74379834],\n",
       "       [7.59951065, 7.75000244],\n",
       "       [7.59525447, 7.75588948],\n",
       "       [7.59134419, 7.76150005],\n",
       "       [7.5877719 , 7.76689174]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_interval[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00495179,  0.00678807, -0.01307057,  0.00240765,  0.00566263,\n",
       "        0.0066329 ,  0.00148707,  0.01773066,  0.02391171, -0.02024075,\n",
       "        0.02894678, -0.00602338, -0.00246339, -0.00112423, -0.00170246,\n",
       "       -0.00526295,  0.00520799,  0.01784181,  0.0032345 , -0.00826875,\n",
       "        0.0027368 ,  0.00194642,  0.01218676, -0.00422438,  0.00755385,\n",
       "        0.01721521, -0.0157803 , -0.00420426,  0.05438435, -0.00432775,\n",
       "        0.00493366,  0.0040803 ,  0.00648906, -0.00094943, -0.00946851,\n",
       "        0.00764207,  0.00452246, -0.00332114, -0.00879292, -0.00851457,\n",
       "       -0.01072046,  0.0004372 , -0.00226524,  0.01853654, -0.01431908,\n",
       "        0.01581051, -0.00197219, -0.00415938,  0.00337448,  0.00017729,\n",
       "        0.00945199,  0.00353096,  0.00169843,  0.00246757,  0.00211377,\n",
       "       -0.0013921 ,  0.00382362,  0.00861149,  0.00387436,  0.00385941,\n",
       "        0.00888334,  0.00135227,  0.00500675, -0.00192271, -0.01234494,\n",
       "        0.02215997, -0.00940615, -0.00190153,  0.00219005,  0.00043263,\n",
       "       -0.01671956, -0.00112464,  0.0104397 ,  0.00422752, -0.00164057,\n",
       "        0.00344691,  0.01207845,  0.04431635, -0.00696088, -0.01898169,\n",
       "        0.02156629, -0.00235561,  0.03237316, -0.04196705, -0.04639343,\n",
       "        0.01816508, -0.01200914,  0.00683562, -0.02331198, -0.03164137,\n",
       "       -0.03163221,  0.01613355,  0.00654433, -0.01690818, -0.00843184,\n",
       "       -0.00534564, -0.03168429,  0.0026272 ,  0.00243145, -0.02951941,\n",
       "        0.03918651,  0.01264024,  0.02262062, -0.00123906, -0.01158695,\n",
       "       -0.01188143,  0.02189153,  0.00487762, -0.0171113 ,  0.00295498,\n",
       "        0.0104422 , -0.02846402, -0.00415613,  0.00731493,  0.00836626,\n",
       "       -0.01280204, -0.03590928,  0.00030349,  0.01834061,  0.02363379,\n",
       "        0.04835551, -0.01183956,  0.00391978,  0.00716341,  0.01424929,\n",
       "       -0.00204102, -0.00702599,  0.00125379, -0.00977092,  0.0121738 ,\n",
       "        0.00836492, -0.01710298,  0.00359989,  0.00228411, -0.00323946,\n",
       "       -0.00980722, -0.00734229, -0.03026323,  0.01190272, -0.01539017,\n",
       "        0.01039989, -0.00678058, -0.00158349,  0.0051372 ,  0.01071598,\n",
       "       -0.01221489, -0.01223273,  0.01115317, -0.00229464, -0.00349161,\n",
       "        0.00084757, -0.02465886, -0.01602967,  0.02964045,  0.00125737,\n",
       "        0.01283074,  0.00334656,  0.00422645, -0.003569  , -0.01392478,\n",
       "        0.01374073,  0.01007242,  0.01764843,  0.01409458, -0.0006811 ,\n",
       "        0.01636878,  0.00783872,  0.00995057, -0.00278402,  0.01468705,\n",
       "        0.00389639, -0.01037579,  0.        , -0.00476539,  0.00670394,\n",
       "       -0.01012174, -0.00731417,  0.        , -0.06351098,  0.00521005,\n",
       "       -0.00028729,  0.00596379,  0.        ,  0.05704878,  0.00947088,\n",
       "       -0.03694547,  0.00600678, -0.00366512,  0.00576775,  0.00682801,\n",
       "        0.00465809, -0.00391332, -0.00116705, -0.00595574, -0.0065699 ,\n",
       "        0.02609805, -0.00844261,  0.        , -0.00383868,  0.        ,\n",
       "       -0.015403  , -0.00080668, -0.00746735,  0.01633018,  0.00067467,\n",
       "        0.01487643,  0.02318303,  0.        , -0.03166027,  0.01663916,\n",
       "        0.01090068,  0.00997065, -0.02480916, -0.01022239, -0.00247893,\n",
       "        0.04787968,  0.00871599,  0.0085244 ,  0.0052882 , -0.00296537,\n",
       "        0.01309227,  0.00527585,  0.01964629,  0.00969133, -0.02008669,\n",
       "       -0.03837247,  0.01205185, -0.00273795,  0.00041814,  0.00910905,\n",
       "       -0.01706031, -0.00330633, -0.01724712,  0.00145663,  0.00635089,\n",
       "       -0.00632703,  0.02288159, -0.01608046, -0.01336057, -0.01339543,\n",
       "       -0.00294975,  0.00348513,  0.00900972,  0.01616757,  0.01821929])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.endog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.7010381 , 7.71250266, 7.72132943, 7.72942348, 7.73707169,\n",
       "       7.74379962, 7.75000382, 7.75589096, 7.76150161, 7.76689339])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(predictions + 1.96 * error)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.63982378, 7.62646592, 7.61902226, 7.61348321, 7.60961645,\n",
       "       7.60439534, 7.59950926, 7.59525299, 7.59134263, 7.58777026])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(predictions - 1.96 * error)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=7.642313, actual=7.631141\n",
      "predicted=7.634232, actual=7.631141\n",
      "predicted=7.632516, actual=7.626375\n",
      "predicted=7.629072, actual=7.633079\n",
      "predicted=7.634839, actual=7.622957\n",
      "predicted=7.621720, actual=7.615643\n",
      "predicted=7.617704, actual=7.615643\n",
      "predicted=7.615724, actual=7.552132\n",
      "predicted=7.554593, actual=7.557342\n",
      "predicted=7.560087, actual=7.557055\n",
      "predicted=7.551836, actual=7.563019\n",
      "predicted=7.564309, actual=7.563019\n",
      "predicted=7.552747, actual=7.620068\n",
      "predicted=7.619829, actual=7.629539\n",
      "predicted=7.627589, actual=7.592593\n",
      "predicted=7.596197, actual=7.598600\n",
      "predicted=7.602220, actual=7.594935\n",
      "predicted=7.597300, actual=7.600702\n",
      "predicted=7.603198, actual=7.607530\n",
      "predicted=7.604694, actual=7.612189\n",
      "predicted=7.613020, actual=7.608275\n",
      "predicted=7.608681, actual=7.607108\n",
      "predicted=7.608652, actual=7.601152\n",
      "predicted=7.602319, actual=7.594583\n",
      "predicted=7.596263, actual=7.620681\n",
      "predicted=7.620880, actual=7.612238\n",
      "predicted=7.610899, actual=7.612238\n",
      "predicted=7.614528, actual=7.608399\n",
      "predicted=7.607916, actual=7.608399\n",
      "predicted=7.611421, actual=7.592996\n",
      "predicted=7.593551, actual=7.592190\n",
      "predicted=7.594197, actual=7.584722\n",
      "predicted=7.584855, actual=7.601052\n",
      "predicted=7.602042, actual=7.601727\n",
      "predicted=7.600058, actual=7.616604\n",
      "predicted=7.617376, actual=7.639787\n",
      "predicted=7.638202, actual=7.639787\n",
      "predicted=7.640333, actual=7.608126\n",
      "predicted=7.610033, actual=7.624765\n",
      "predicted=7.627836, actual=7.635666\n",
      "predicted=7.634644, actual=7.645637\n",
      "predicted=7.646219, actual=7.620828\n",
      "predicted=7.620353, actual=7.610605\n",
      "predicted=7.615017, actual=7.608126\n",
      "predicted=7.609976, actual=7.656006\n",
      "predicted=7.656009, actual=7.664722\n",
      "predicted=7.659212, actual=7.673246\n",
      "predicted=7.672306, actual=7.678535\n",
      "predicted=7.678270, actual=7.675569\n",
      "predicted=7.678589, actual=7.688661\n",
      "predicted=7.690101, actual=7.693937\n",
      "predicted=7.694241, actual=7.713584\n",
      "predicted=7.714129, actual=7.723275\n",
      "predicted=7.722398, actual=7.703188\n",
      "predicted=7.704747, actual=7.664816\n",
      "predicted=7.668089, actual=7.676868\n",
      "predicted=7.682775, actual=7.674130\n",
      "predicted=7.675242, actual=7.674548\n",
      "predicted=7.675004, actual=7.683657\n",
      "predicted=7.682369, actual=7.666597\n",
      "predicted=7.667849, actual=7.663290\n",
      "predicted=7.665677, actual=7.646043\n",
      "predicted=7.647540, actual=7.647500\n",
      "predicted=7.650776, actual=7.653851\n",
      "predicted=7.653663, actual=7.647524\n",
      "predicted=7.648021, actual=7.670405\n",
      "predicted=7.670248, actual=7.654325\n",
      "predicted=7.653655, actual=7.640964\n",
      "predicted=7.643984, actual=7.627569\n",
      "predicted=7.628996, actual=7.624619\n",
      "predicted=7.628661, actual=7.628104\n",
      "predicted=7.628498, actual=7.637114\n",
      "predicted=7.637006, actual=7.653281\n",
      "predicted=7.652325, actual=7.671501\n",
      "Test RMSE: 0.018\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c23fc6190>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "x = y.values\n",
    "train_size = int(len(x) * 0.7)\n",
    "train, test = x[0:train_size], x[train_size:]\n",
    "history = [x for x in train]\n",
    "predictions = []\n",
    "for t in range(len(test)):\n",
    "    model = ARIMA(history, order=(5,1,0))\n",
    "    model_fit = model.fit(disp=0)\n",
    "    output = model_fit.forecast()\n",
    "    yhat = output[0][0]\n",
    "    predictions.append(yhat)\n",
    "    obs = test[t]\n",
    "    history.append(obs)\n",
    "    print('predicted=%f, actual=%f' % (yhat, obs))\n",
    "error = mean_squared_error(test, predictions)\n",
    "print('Test RMSE: %.3f' % np.sqrt(error))\n",
    "\n",
    "plt.plot(test)\n",
    "plt.plot(predictions, color='red')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>7.631141</td>\n",
       "      <td>7.642313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>7.631141</td>\n",
       "      <td>7.634232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-02-12</td>\n",
       "      <td>7.626375</td>\n",
       "      <td>7.632516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-02-13</td>\n",
       "      <td>7.633079</td>\n",
       "      <td>7.629072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>7.622957</td>\n",
       "      <td>7.634839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>7.624619</td>\n",
       "      <td>7.628996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-27</td>\n",
       "      <td>7.628104</td>\n",
       "      <td>7.628661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-28</td>\n",
       "      <td>7.637114</td>\n",
       "      <td>7.628498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>7.653281</td>\n",
       "      <td>7.637006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>7.671501</td>\n",
       "      <td>7.652325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              actual  prediction\n",
       "Date                            \n",
       "2019-02-08  7.631141    7.642313\n",
       "2019-02-11  7.631141    7.634232\n",
       "2019-02-12  7.626375    7.632516\n",
       "2019-02-13  7.633079    7.629072\n",
       "2019-02-14  7.622957    7.634839\n",
       "...              ...         ...\n",
       "2019-05-24  7.624619    7.628996\n",
       "2019-05-27  7.628104    7.628661\n",
       "2019-05-28  7.637114    7.628498\n",
       "2019-05-29  7.653281    7.637006\n",
       "2019-05-30  7.671501    7.652325\n",
       "\n",
       "[74 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(y)[train_size:]\n",
    "result.columns = [\"actual\"]\n",
    "result[\"prediction\"] = predictions\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from numpy import array\n",
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2018-05-31    0.746224\n",
      "2018-06-01    0.745729\n",
      "2018-06-04    0.746408\n",
      "2018-06-05    0.745101\n",
      "2018-06-06    0.745342\n",
      "2018-06-07    0.745908\n",
      "2018-06-08    0.746571\n",
      "2018-06-11    0.746720\n",
      "2018-06-12    0.748493\n",
      "2018-06-13    0.750884\n",
      "Name: Close Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_b = np.log(df[\"Close Price\"])\n",
    "y_b = y_b.asfreq(\"B\").dropna()/10.0\n",
    "print(\"%s\" % y_b[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples: 167\n",
      "[[0.74622437 0.74572919 0.74640799 0.74510094 0.7453417 ]] => [0.74590796]\n",
      "[[0.74572919 0.74640799 0.74510094 0.7453417  0.74590796]] => [0.74657125]\n",
      "[[0.74640799 0.74510094 0.7453417  0.74590796 0.74657125]] => [0.74671996]\n"
     ]
    }
   ],
   "source": [
    "n_input = 5\n",
    "train_size = int(0.7 * len(y_b))\n",
    "y_b_train = y_b[:train_size]\n",
    "y_b_test = y_b[train_size:]\n",
    "generator_train = TimeseriesGenerator(y_b_train, y_b_train, length=n_input, batch_size=1, )\n",
    "generator_test = TimeseriesGenerator(y_b_test, y_b_test, length=n_input, batch_size=1)\n",
    "print('Samples: %d' % len(generator_train))\n",
    "for i in range(3):\n",
    "    x, y = generator_train[i]\n",
    "    print('%s => %s' % (x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 6\n",
      "Trainable params: 6\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "[array([[-0.3597389 ],\n",
      "       [-0.43928966],\n",
      "       [-0.32653952],\n",
      "       [ 0.1131153 ],\n",
      "       [ 0.94963855]], dtype=float32), array([0.804969], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "tf.set_random_seed(1)\n",
    "np.random.seed(1)\n",
    "\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.InputLayer(input_shape = (n_input,)),\n",
    "    keras.layers.Dense(1, activation = None, kernel_initializer = keras.initializers.he_uniform(seed = 1.0))\n",
    "])\n",
    "model.compile(loss = \"mse\", optimizer = keras.optimizers.SGD(lr = 0.1))\n",
    "model.summary()\n",
    "\n",
    "model.fit_generator(generator_train, steps_per_epoch=1, epochs=500, verbose=0)\n",
    "y_b_test_pred = model.predict_generator(generator_test, use_multiprocessing = True)\n",
    "layer = model.layers[0]\n",
    "print(layer.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((69, 1), (74,))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_b_test_pred.shape, y_b_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07251101321580536"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_b_test[n_input:] * 10.0, y_b_test_pred * 10.0) ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Samples: 167\n",
      "[[[0.74622437]\n",
      "  [0.74572919]\n",
      "  [0.74640799]\n",
      "  [0.74510094]\n",
      "  [0.7453417 ]]] => [[0.74590796]]\n",
      "[[[0.74572919]\n",
      "  [0.74640799]\n",
      "  [0.74510094]\n",
      "  [0.7453417 ]\n",
      "  [0.74590796]]] => [[0.74657125]]\n",
      "[[[0.74640799]\n",
      "  [0.74510094]\n",
      "  [0.7453417 ]\n",
      "  [0.74590796]\n",
      "  [0.74657125]]] => [[0.74671996]]\n"
     ]
    }
   ],
   "source": [
    "y_b = y_b.values\n",
    "y_b = y_b.reshape((len(y_b), 1))\n",
    "\n",
    "train_size = int(0.7 * len(y_b))\n",
    "y_b_train = y_b[:train_size]\n",
    "y_b_test = y_b[train_size:]\n",
    "\n",
    "n_input = 5\n",
    "\n",
    "generator_train = TimeseriesGenerator(y_b_train, y_b_train, length=n_input, batch_size=1, )\n",
    "generator_test = TimeseriesGenerator(y_b_test, y_b_test, length=n_input, batch_size=1)\n",
    "print('Samples: %d' % len(generator_train))\n",
    "for i in range(3):\n",
    "    x, y = generator_train[i]\n",
    "    print('%s => %s' % (x, y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 100)               40800     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 40,901\n",
      "Trainable params: 40,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/500\n",
      "1/1 [==============================] - 1s 585ms/step - loss: 0.5183\n",
      "Epoch 2/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4663\n",
      "Epoch 3/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.4234\n",
      "Epoch 4/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.3667\n",
      "Epoch 5/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.3279\n",
      "Epoch 6/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.2827\n",
      "Epoch 7/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.2467\n",
      "Epoch 8/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.2054\n",
      "Epoch 9/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1703\n",
      "Epoch 10/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1340\n",
      "Epoch 11/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.1071\n",
      "Epoch 12/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0742\n",
      "Epoch 13/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0494\n",
      "Epoch 14/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0316\n",
      "Epoch 15/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0159\n",
      "Epoch 16/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0045\n",
      "Epoch 17/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.3205e-05\n",
      "Epoch 18/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0015\n",
      "Epoch 19/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0074\n",
      "Epoch 20/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0160\n",
      "Epoch 21/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0243\n",
      "Epoch 22/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0304\n",
      "Epoch 23/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0312\n",
      "Epoch 24/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0293\n",
      "Epoch 25/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0237\n",
      "Epoch 26/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0185\n",
      "Epoch 27/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0135\n",
      "Epoch 28/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0071\n",
      "Epoch 29/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0038\n",
      "Epoch 30/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0016\n",
      "Epoch 31/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.9945e-04\n",
      "Epoch 32/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.8022e-05\n",
      "Epoch 33/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.5785e-04\n",
      "Epoch 34/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0014\n",
      "Epoch 35/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0025\n",
      "Epoch 36/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 0.0035\n",
      "Epoch 37/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0043\n",
      "Epoch 38/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0046\n",
      "Epoch 39/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0057\n",
      "Epoch 40/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0050\n",
      "Epoch 41/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0044\n",
      "Epoch 42/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0044\n",
      "Epoch 43/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0035\n",
      "Epoch 44/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0031\n",
      "Epoch 45/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0019\n",
      "Epoch 46/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0014\n",
      "Epoch 47/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 3.9434e-04\n",
      "Epoch 48/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.7921e-04\n",
      "Epoch 49/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.9382e-05\n",
      "Epoch 50/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.1536e-06\n",
      "Epoch 51/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.0172e-04\n",
      "Epoch 52/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.3048e-04\n",
      "Epoch 53/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.8098e-04\n",
      "Epoch 54/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4.9414e-04\n",
      "Epoch 55/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.9877e-04\n",
      "Epoch 56/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0011\n",
      "Epoch 57/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 0.0011\n",
      "Epoch 58/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 9.4789e-04\n",
      "Epoch 59/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.8432e-04\n",
      "Epoch 60/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.9499e-04\n",
      "Epoch 61/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 3.8717e-04\n",
      "Epoch 62/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.4475e-04\n",
      "Epoch 63/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.3797e-04\n",
      "Epoch 64/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.4598e-05\n",
      "Epoch 65/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.9787e-07\n",
      "Epoch 66/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.0700e-04\n",
      "Epoch 67/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.1718e-05\n",
      "Epoch 68/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.3159e-05\n",
      "Epoch 69/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.1441e-04\n",
      "Epoch 70/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 5.8854e-05\n",
      "Epoch 71/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.3546e-04\n",
      "Epoch 72/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.5025e-04\n",
      "Epoch 73/500\n",
      "1/1 [==============================] - 0s 35ms/step - loss: 2.7873e-04\n",
      "Epoch 74/500\n",
      "1/1 [==============================] - 0s 9ms/step - loss: 1.3182e-04\n",
      "Epoch 75/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.6983e-04\n",
      "Epoch 76/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 9.5882e-05\n",
      "Epoch 77/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.2263e-05\n",
      "Epoch 78/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 9.6591e-06\n",
      "Epoch 79/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 5.2586e-06\n",
      "Epoch 80/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.2792e-05\n",
      "Epoch 81/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.9179e-05\n",
      "Epoch 82/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.8325e-07\n",
      "Epoch 83/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.7991e-05\n",
      "Epoch 84/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.4702e-05\n",
      "Epoch 85/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 9.3653e-05\n",
      "Epoch 86/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.4078e-05\n",
      "Epoch 87/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.4975e-05\n",
      "Epoch 88/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5.1968e-05\n",
      "Epoch 89/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.2489e-05\n",
      "Epoch 90/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.6998e-06\n",
      "Epoch 91/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4.2896e-06\n",
      "Epoch 92/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 1.2863e-05\n",
      "Epoch 93/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.4561e-06\n",
      "Epoch 94/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.1697e-07\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 95/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.7082e-07\n",
      "Epoch 96/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.1189e-06\n",
      "Epoch 97/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.4168e-08\n",
      "Epoch 98/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.2087e-05\n",
      "Epoch 99/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.7892e-05\n",
      "Epoch 100/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.1001e-05\n",
      "Epoch 101/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 9.3358e-06\n",
      "Epoch 102/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 3.4208e-08\n",
      "Epoch 103/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 4.3895e-06\n",
      "Epoch 104/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.4468e-05\n",
      "Epoch 105/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.3958e-06\n",
      "Epoch 106/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.5805e-06\n",
      "Epoch 107/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.4157e-06\n",
      "Epoch 108/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.3240e-06\n",
      "Epoch 109/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.7774e-08\n",
      "Epoch 110/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 2.8253e-06\n",
      "Epoch 111/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.8197e-07\n",
      "Epoch 112/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.0267e-07\n",
      "Epoch 113/500\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 4.3496e-06\n",
      "Epoch 114/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 8.3928e-07\n",
      "Epoch 115/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 5.1058e-06\n",
      "Epoch 116/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 7.2349e-06\n",
      "Epoch 117/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.2595e-06\n",
      "Epoch 118/500\n",
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     ]
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     "text": [
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     ]
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      "Epoch 291/500\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 8.0496e-08\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 389/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 1.5183e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.4126e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.0600e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.0352e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.5149e-07\n",
      "Epoch 400/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.7927e-06\n",
      "Epoch 401/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.8015e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 6.8649e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 3.0971e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.9835e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 3.2352e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 6.6993e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 6.8850e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.4094e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 7.6957e-06\n",
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      "Epoch 416/500\n",
      "1/1 [==============================] - 0s 31ms/step - loss: 4.4113e-07\n",
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      "1/1 [==============================] - 0s 8ms/step - loss: 5.4760e-06\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 4.8916e-06\n",
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      "Epoch 421/500\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.6152e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.3546e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 8.9410e-07\n",
      "Epoch 425/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.4488e-06\n",
      "Epoch 426/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.6902e-06\n",
      "Epoch 427/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 6.4914e-05\n",
      "Epoch 428/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.2060e-08\n",
      "Epoch 429/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.6883e-06\n",
      "Epoch 430/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.0360e-09\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 4.4820e-07\n",
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      "1/1 [==============================] - 0s 5ms/step - loss: 3.3641e-06\n",
      "Epoch 433/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 8.7844e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 7.4865e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.2340e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 8.6229e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.8293e-08\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 9.3194e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.3669e-05\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 2.0882e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 5.4562e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.2471e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 9.4015e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 3.6926e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.0600e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 3.7123e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.8890e-06\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.6656e-07\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 2.5923e-05\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 2.8344e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 8.7008e-06\n",
      "Epoch 453/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.0124e-06\n",
      "Epoch 454/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.2641e-05\n",
      "Epoch 455/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 4.1562e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 4.0759e-07\n",
      "Epoch 457/500\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.0140e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.3047e-08\n",
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      "1/1 [==============================] - 0s 35ms/step - loss: 2.9354e-05\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 6.9772e-06\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 4.4993e-06\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 2.6705e-09\n",
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      "1/1 [==============================] - 0s 7ms/step - loss: 1.0548e-05\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.4305e-06\n",
      "Epoch 469/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.2167e-05\n",
      "Epoch 470/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 2.9186e-06\n",
      "Epoch 471/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.5564e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 2.4575e-05\n",
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      "1/1 [==============================] - 0s 5ms/step - loss: 7.0291e-07\n",
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      "1/1 [==============================] - 0s 6ms/step - loss: 1.8626e-05\n",
      "Epoch 475/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 7.2234e-06\n",
      "Epoch 476/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.2721e-05\n",
      "Epoch 477/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.6278e-07\n",
      "Epoch 478/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.9541e-06\n",
      "Epoch 479/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.4962e-07\n",
      "Epoch 480/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.1199e-06\n",
      "Epoch 481/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.6929e-07\n",
      "Epoch 482/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.5590e-07\n",
      "Epoch 483/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.1773e-05\n",
      "Epoch 484/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 5.4670e-06\n",
      "Epoch 485/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.0676e-06\n",
      "Epoch 486/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.1195e-06\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 487/500\n",
      "1/1 [==============================] - 0s 7ms/step - loss: 5.2242e-06\n",
      "Epoch 488/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 4.4828e-07\n",
      "Epoch 489/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.6785e-06\n",
      "Epoch 490/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.5152e-07\n",
      "Epoch 491/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.9737e-08\n",
      "Epoch 492/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.7381e-07\n",
      "Epoch 493/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.0329e-07\n",
      "Epoch 494/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.9879e-05\n",
      "Epoch 495/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.0817e-06\n",
      "Epoch 496/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.2209e-05\n",
      "Epoch 497/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 3.6378e-07\n",
      "Epoch 498/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 2.3634e-05\n",
      "Epoch 499/500\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 1.5354e-05\n",
      "Epoch 500/500\n",
      "1/1 [==============================] - 0s 5ms/step - loss: 1.4438e-07\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.InputLayer(input_shape = (n_input,1)),\n",
    "    keras.layers.LSTM(units = 100),\n",
    "    keras.layers.Dense(units = 1, activation = None)\n",
    "])\n",
    "model.compile(loss = \"mse\", optimizer = \"adam\")\n",
    "model.summary()\n",
    "\n",
    "model.fit_generator(generator_train, steps_per_epoch=1, epochs=500, verbose=1)\n",
    "y_b_test_pred = model.predict_generator(generator_test)\n",
    "layer = model.layers[0]\n",
    "#print(layer.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((69, 1), (74, 1))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_b_test_pred.shape, y_b_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02561094861964438"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.mean_squared_error(y_b_test[n_input:] * 10.0, y_b_test_pred * 10.0) ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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