LSTM Stock Predictor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f61f37b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cbb0ef3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('dark_background')\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f2c66999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "      <th>1980-12-12</th>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.100764</td>\n",
       "      <td>469033600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.095508</td>\n",
       "      <td>175884800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.088498</td>\n",
       "      <td>105728000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.116071</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.090688</td>\n",
       "      <td>86441600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.093317</td>\n",
       "      <td>73449600</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-12</th>\n",
       "      <td>159.589996</td>\n",
       "      <td>164.259995</td>\n",
       "      <td>159.300003</td>\n",
       "      <td>163.429993</td>\n",
       "      <td>163.429993</td>\n",
       "      <td>104956000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>159.899994</td>\n",
       "      <td>160.539993</td>\n",
       "      <td>153.369995</td>\n",
       "      <td>153.839996</td>\n",
       "      <td>153.839996</td>\n",
       "      <td>122656600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>154.789993</td>\n",
       "      <td>157.100006</td>\n",
       "      <td>153.610001</td>\n",
       "      <td>155.309998</td>\n",
       "      <td>155.309998</td>\n",
       "      <td>87965400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>154.649994</td>\n",
       "      <td>155.240005</td>\n",
       "      <td>151.380005</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>90481100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>151.210007</td>\n",
       "      <td>151.350006</td>\n",
       "      <td>148.369995</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>162157000</td>\n",
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       "  </tbody>\n",
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       "<p>10530 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "1980-12-12    0.128348    0.128906    0.128348    0.128348    0.100764   \n",
       "1980-12-15    0.122210    0.122210    0.121652    0.121652    0.095508   \n",
       "1980-12-16    0.113281    0.113281    0.112723    0.112723    0.088498   \n",
       "1980-12-17    0.115513    0.116071    0.115513    0.115513    0.090688   \n",
       "1980-12-18    0.118862    0.119420    0.118862    0.118862    0.093317   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-09-12  159.589996  164.259995  159.300003  163.429993  163.429993   \n",
       "2022-09-13  159.899994  160.539993  153.369995  153.839996  153.839996   \n",
       "2022-09-14  154.789993  157.100006  153.610001  155.309998  155.309998   \n",
       "2022-09-15  154.649994  155.240005  151.380005  152.369995  152.369995   \n",
       "2022-09-16  151.210007  151.350006  148.369995  150.699997  150.699997   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "1980-12-12  469033600  \n",
       "1980-12-15  175884800  \n",
       "1980-12-16  105728000  \n",
       "1980-12-17   86441600  \n",
       "1980-12-18   73449600  \n",
       "...               ...  \n",
       "2022-09-12  104956000  \n",
       "2022-09-13  122656600  \n",
       "2022-09-14   87965400  \n",
       "2022-09-15   90481100  \n",
       "2022-09-16  162157000  \n",
       "\n",
       "[10530 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = yf.download(\"AAPL\",period=\"max\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4a414bfa",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Open</th>\n",
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       "      <th>Low</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-12-12</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.100764</td>\n",
       "      <td>469033600</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-12-15</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.095508</td>\n",
       "      <td>175884800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-12-16</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.088498</td>\n",
       "      <td>105728000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-12-17</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.116071</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.090688</td>\n",
       "      <td>86441600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-12-18</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.093317</td>\n",
       "      <td>73449600</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10525</th>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>159.589996</td>\n",
       "      <td>164.259995</td>\n",
       "      <td>159.300003</td>\n",
       "      <td>163.429993</td>\n",
       "      <td>163.429993</td>\n",
       "      <td>104956000</td>\n",
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       "    <tr>\n",
       "      <th>10526</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>159.899994</td>\n",
       "      <td>160.539993</td>\n",
       "      <td>153.369995</td>\n",
       "      <td>153.839996</td>\n",
       "      <td>153.839996</td>\n",
       "      <td>122656600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10527</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>154.789993</td>\n",
       "      <td>157.100006</td>\n",
       "      <td>153.610001</td>\n",
       "      <td>155.309998</td>\n",
       "      <td>155.309998</td>\n",
       "      <td>87965400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10528</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>154.649994</td>\n",
       "      <td>155.240005</td>\n",
       "      <td>151.380005</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>90481100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10529</th>\n",
       "      <td>2022-09-16</td>\n",
       "      <td>151.210007</td>\n",
       "      <td>151.350006</td>\n",
       "      <td>148.369995</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>162157000</td>\n",
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       "<p>10530 rows × 7 columns</p>\n",
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      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     1980-12-12    0.128348    0.128906    0.128348    0.128348    0.100764   \n",
       "1     1980-12-15    0.122210    0.122210    0.121652    0.121652    0.095508   \n",
       "2     1980-12-16    0.113281    0.113281    0.112723    0.112723    0.088498   \n",
       "3     1980-12-17    0.115513    0.116071    0.115513    0.115513    0.090688   \n",
       "4     1980-12-18    0.118862    0.119420    0.118862    0.118862    0.093317   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "10525 2022-09-12  159.589996  164.259995  159.300003  163.429993  163.429993   \n",
       "10526 2022-09-13  159.899994  160.539993  153.369995  153.839996  153.839996   \n",
       "10527 2022-09-14  154.789993  157.100006  153.610001  155.309998  155.309998   \n",
       "10528 2022-09-15  154.649994  155.240005  151.380005  152.369995  152.369995   \n",
       "10529 2022-09-16  151.210007  151.350006  148.369995  150.699997  150.699997   \n",
       "\n",
       "          Volume  \n",
       "0      469033600  \n",
       "1      175884800  \n",
       "2      105728000  \n",
       "3       86441600  \n",
       "4       73449600  \n",
       "...          ...  \n",
       "10525  104956000  \n",
       "10526  122656600  \n",
       "10527   87965400  \n",
       "10528   90481100  \n",
       "10529  162157000  \n",
       "\n",
       "[10530 rows x 7 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bd921d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.pop('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6a7c6456",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.128348</td>\n",
       "      <td>0.100764</td>\n",
       "      <td>469033600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.122210</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.121652</td>\n",
       "      <td>0.095508</td>\n",
       "      <td>175884800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.113281</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.112723</td>\n",
       "      <td>0.088498</td>\n",
       "      <td>105728000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.116071</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.115513</td>\n",
       "      <td>0.090688</td>\n",
       "      <td>86441600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.119420</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.118862</td>\n",
       "      <td>0.093317</td>\n",
       "      <td>73449600</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-12</th>\n",
       "      <td>159.589996</td>\n",
       "      <td>164.259995</td>\n",
       "      <td>159.300003</td>\n",
       "      <td>163.429993</td>\n",
       "      <td>163.429993</td>\n",
       "      <td>104956000</td>\n",
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       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>159.899994</td>\n",
       "      <td>160.539993</td>\n",
       "      <td>153.369995</td>\n",
       "      <td>153.839996</td>\n",
       "      <td>153.839996</td>\n",
       "      <td>122656600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>154.789993</td>\n",
       "      <td>157.100006</td>\n",
       "      <td>153.610001</td>\n",
       "      <td>155.309998</td>\n",
       "      <td>155.309998</td>\n",
       "      <td>87965400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>154.649994</td>\n",
       "      <td>155.240005</td>\n",
       "      <td>151.380005</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>152.369995</td>\n",
       "      <td>90481100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>151.210007</td>\n",
       "      <td>151.350006</td>\n",
       "      <td>148.369995</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>150.699997</td>\n",
       "      <td>162157000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10530 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "1980-12-12    0.128348    0.128906    0.128348    0.128348    0.100764   \n",
       "1980-12-15    0.122210    0.122210    0.121652    0.121652    0.095508   \n",
       "1980-12-16    0.113281    0.113281    0.112723    0.112723    0.088498   \n",
       "1980-12-17    0.115513    0.116071    0.115513    0.115513    0.090688   \n",
       "1980-12-18    0.118862    0.119420    0.118862    0.118862    0.093317   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2022-09-12  159.589996  164.259995  159.300003  163.429993  163.429993   \n",
       "2022-09-13  159.899994  160.539993  153.369995  153.839996  153.839996   \n",
       "2022-09-14  154.789993  157.100006  153.610001  155.309998  155.309998   \n",
       "2022-09-15  154.649994  155.240005  151.380005  152.369995  152.369995   \n",
       "2022-09-16  151.210007  151.350006  148.369995  150.699997  150.699997   \n",
       "\n",
       "               Volume  \n",
       "Date                   \n",
       "1980-12-12  469033600  \n",
       "1980-12-15  175884800  \n",
       "1980-12-16  105728000  \n",
       "1980-12-17   86441600  \n",
       "1980-12-18   73449600  \n",
       "...               ...  \n",
       "2022-09-12  104956000  \n",
       "2022-09-13  122656600  \n",
       "2022-09-14   87965400  \n",
       "2022-09-15   90481100  \n",
       "2022-09-16  162157000  \n",
       "\n",
       "[10530 rows x 6 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7ae6ee04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26aa9a92d60>]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.index, df.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5715844a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1986, 3, 19, 0, 0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "def str_to_datetime(s):\n",
    "  split = s.split('-')\n",
    "  year, month, day = int(split[0]), int(split[1]), int(split[2])\n",
    "  return datetime.datetime(year=year, month=month, day=day)\n",
    "\n",
    "datetime_object = str_to_datetime('1986-03-19')\n",
    "datetime_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "027e7323",
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_to_windowed_df(dataframe, first_date_str, last_date_str, n=3):\n",
    "  first_date = str_to_datetime(first_date_str)\n",
    "  last_date  = str_to_datetime(last_date_str)\n",
    "\n",
    "  target_date = first_date\n",
    "  \n",
    "  dates = []\n",
    "  X, Y = [], []\n",
    "\n",
    "  last_time = False\n",
    "  while True:\n",
    "    df_subset = dataframe.loc[:target_date].tail(n+1)\n",
    "    \n",
    "    if len(df_subset) != n+1:\n",
    "      print(f'Error: Window of size {n} is too large for date {target_date}')\n",
    "      return\n",
    "\n",
    "    values = df_subset['Close'].to_numpy()\n",
    "    x, y = values[:-1], values[-1]\n",
    "\n",
    "    dates.append(target_date)\n",
    "    X.append(x)\n",
    "    Y.append(y)\n",
    "\n",
    "    next_week = dataframe.loc[target_date:target_date+datetime.timedelta(days=7)]\n",
    "    next_datetime_str = str(next_week.head(2).tail(1).index.values[0])\n",
    "    next_date_str = next_datetime_str.split('T')[0]\n",
    "    year_month_day = next_date_str.split('-')\n",
    "    year, month, day = year_month_day\n",
    "    next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))\n",
    "    \n",
    "    if last_time:\n",
    "      break\n",
    "    \n",
    "    target_date = next_date\n",
    "\n",
    "    if target_date == last_date:\n",
    "      last_time = True\n",
    "    \n",
    "  ret_df = pd.DataFrame({})\n",
    "  ret_df['Target Date'] = dates\n",
    "  \n",
    "  X = np.array(X)\n",
    "  for i in range(0, n):\n",
    "    X[:, i]\n",
    "    ret_df[f'Target-{n-i}'] = X[:, i]\n",
    "  \n",
    "  ret_df['Target'] = Y\n",
    "\n",
    "  return ret_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dbc1fcb2",
   "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>Target Date</th>\n",
       "      <th>Target-3</th>\n",
       "      <th>Target-2</th>\n",
       "      <th>Target-1</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>123.389999</td>\n",
       "      <td>122.540001</td>\n",
       "      <td>120.089996</td>\n",
       "      <td>120.589996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>122.540001</td>\n",
       "      <td>120.089996</td>\n",
       "      <td>120.589996</td>\n",
       "      <td>121.209999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>120.089996</td>\n",
       "      <td>120.589996</td>\n",
       "      <td>121.209999</td>\n",
       "      <td>121.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>120.589996</td>\n",
       "      <td>121.209999</td>\n",
       "      <td>121.389999</td>\n",
       "      <td>119.900002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>121.209999</td>\n",
       "      <td>121.389999</td>\n",
       "      <td>119.900002</td>\n",
       "      <td>122.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>2022-08-17</td>\n",
       "      <td>172.100006</td>\n",
       "      <td>173.190002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>174.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>2022-08-18</td>\n",
       "      <td>173.190002</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>174.550003</td>\n",
       "      <td>174.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>2022-08-19</td>\n",
       "      <td>173.029999</td>\n",
       "      <td>174.550003</td>\n",
       "      <td>174.149994</td>\n",
       "      <td>171.520004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2022-08-22</td>\n",
       "      <td>174.550003</td>\n",
       "      <td>174.149994</td>\n",
       "      <td>171.520004</td>\n",
       "      <td>167.570007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>2022-08-23</td>\n",
       "      <td>174.149994</td>\n",
       "      <td>171.520004</td>\n",
       "      <td>167.570007</td>\n",
       "      <td>167.229996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>357 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Target Date    Target-3    Target-2    Target-1      Target\n",
       "0    2021-03-25  123.389999  122.540001  120.089996  120.589996\n",
       "1    2021-03-26  122.540001  120.089996  120.589996  121.209999\n",
       "2    2021-03-29  120.089996  120.589996  121.209999  121.389999\n",
       "3    2021-03-30  120.589996  121.209999  121.389999  119.900002\n",
       "4    2021-03-31  121.209999  121.389999  119.900002  122.150002\n",
       "..          ...         ...         ...         ...         ...\n",
       "352  2022-08-17  172.100006  173.190002  173.029999  174.550003\n",
       "353  2022-08-18  173.190002  173.029999  174.550003  174.149994\n",
       "354  2022-08-19  173.029999  174.550003  174.149994  171.520004\n",
       "355  2022-08-22  174.550003  174.149994  171.520004  167.570007\n",
       "356  2022-08-23  174.149994  171.520004  167.570007  167.229996\n",
       "\n",
       "[357 rows x 5 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "windowed_df = df_to_windowed_df(df, \n",
    "                                '2021-03-25', \n",
    "                                '2022-08-23', \n",
    "                                n=3)\n",
    "windowed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a10e37dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((357,), (357, 3, 1), (357,))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def windowed_df_to_date_X_y(windowed_dataframe):\n",
    "  df_as_np = windowed_dataframe.to_numpy()\n",
    "\n",
    "  dates = df_as_np[:, 0]\n",
    "\n",
    "  middle_matrix = df_as_np[:, 1:-1]\n",
    "  X = middle_matrix.reshape((len(dates), middle_matrix.shape[1], 1))\n",
    "\n",
    "  Y = df_as_np[:, -1]\n",
    "\n",
    "  return dates, X.astype(np.float32), Y.astype(np.float32)\n",
    "\n",
    "dates, X, y = windowed_df_to_date_X_y(windowed_df)\n",
    "\n",
    "dates.shape, X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a391dcf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26aa9ad9fa0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q_80 = int(len(dates) * .8)\n",
    "q_90 = int(len(dates) * .9)\n",
    "\n",
    "dates_train, X_train, y_train = dates[:q_80], X[:q_80], y[:q_80]\n",
    "\n",
    "dates_val, X_val, y_val = dates[q_80:q_90], X[q_80:q_90], y[q_80:q_90]\n",
    "dates_test, X_test, y_test = dates[q_90:], X[q_90:], y[q_90:]\n",
    "\n",
    "plt.plot(dates_train, y_train)\n",
    "plt.plot(dates_val, y_val)\n",
    "plt.plot(dates_test, y_test)\n",
    "\n",
    "plt.legend(['Train', 'Validation', 'Test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "946df85b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "9/9 [==============================] - 5s 143ms/step - loss: 23297.0020 - mean_absolute_error: 151.7476 - val_loss: 19998.2500 - val_mean_absolute_error: 141.3012\n",
      "Epoch 2/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 23147.4629 - mean_absolute_error: 151.2528 - val_loss: 19861.7090 - val_mean_absolute_error: 140.8171\n",
      "Epoch 3/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 22992.8184 - mean_absolute_error: 150.7389 - val_loss: 19702.1328 - val_mean_absolute_error: 140.2494\n",
      "Epoch 4/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 22797.5078 - mean_absolute_error: 150.0902 - val_loss: 19488.8301 - val_mean_absolute_error: 139.4869\n",
      "Epoch 5/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 22551.3691 - mean_absolute_error: 149.2676 - val_loss: 19238.4277 - val_mean_absolute_error: 138.5863\n",
      "Epoch 6/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 22235.4922 - mean_absolute_error: 148.2033 - val_loss: 18856.8672 - val_mean_absolute_error: 137.2026\n",
      "Epoch 7/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 21743.6426 - mean_absolute_error: 146.5360 - val_loss: 18259.3789 - val_mean_absolute_error: 135.0067\n",
      "Epoch 8/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 20921.2031 - mean_absolute_error: 143.7003 - val_loss: 17286.4922 - val_mean_absolute_error: 131.3544\n",
      "Epoch 9/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 19771.8652 - mean_absolute_error: 139.6175 - val_loss: 16072.8018 - val_mean_absolute_error: 126.6503\n",
      "Epoch 10/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 18317.9746 - mean_absolute_error: 134.3274 - val_loss: 14587.6006 - val_mean_absolute_error: 120.6441\n",
      "Epoch 11/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 16541.5664 - mean_absolute_error: 127.5240 - val_loss: 12736.4199 - val_mean_absolute_error: 112.7111\n",
      "Epoch 12/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 14405.1826 - mean_absolute_error: 118.8608 - val_loss: 10606.6211 - val_mean_absolute_error: 102.8287\n",
      "Epoch 13/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 11903.3086 - mean_absolute_error: 107.7839 - val_loss: 8190.3281 - val_mean_absolute_error: 90.3181\n",
      "Epoch 14/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9172.3740 - mean_absolute_error: 94.1431 - val_loss: 5717.1841 - val_mean_absolute_error: 75.3963\n",
      "Epoch 15/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 6438.7393 - mean_absolute_error: 78.4678 - val_loss: 3484.6396 - val_mean_absolute_error: 58.7539\n",
      "Epoch 16/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 4056.3027 - mean_absolute_error: 61.3806 - val_loss: 1757.6138 - val_mean_absolute_error: 41.5344\n",
      "Epoch 17/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 2217.7429 - mean_absolute_error: 43.6506 - val_loss: 555.5523 - val_mean_absolute_error: 22.8651\n",
      "Epoch 18/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 951.5721 - mean_absolute_error: 26.1014 - val_loss: 69.9657 - val_mean_absolute_error: 6.9349\n",
      "Epoch 19/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 393.2744 - mean_absolute_error: 16.4386 - val_loss: 71.1814 - val_mean_absolute_error: 6.8727\n",
      "Epoch 20/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 274.3115 - mean_absolute_error: 13.8281 - val_loss: 189.1441 - val_mean_absolute_error: 12.5286\n",
      "Epoch 21/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 280.0224 - mean_absolute_error: 14.2374 - val_loss: 213.3612 - val_mean_absolute_error: 13.4579\n",
      "Epoch 22/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 281.5928 - mean_absolute_error: 14.0986 - val_loss: 116.5237 - val_mean_absolute_error: 9.2213\n",
      "Epoch 23/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 270.3238 - mean_absolute_error: 13.7286 - val_loss: 121.7288 - val_mean_absolute_error: 9.4871\n",
      "Epoch 24/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.7173 - mean_absolute_error: 13.7541 - val_loss: 131.2446 - val_mean_absolute_error: 9.9543\n",
      "Epoch 25/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.5269 - mean_absolute_error: 13.7948 - val_loss: 138.0829 - val_mean_absolute_error: 10.2922\n",
      "Epoch 26/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 269.5859 - mean_absolute_error: 13.8131 - val_loss: 140.4897 - val_mean_absolute_error: 10.4087\n",
      "Epoch 27/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 269.5112 - mean_absolute_error: 13.8138 - val_loss: 135.6068 - val_mean_absolute_error: 10.1724\n",
      "Epoch 28/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 269.2304 - mean_absolute_error: 13.7937 - val_loss: 135.4785 - val_mean_absolute_error: 10.1679\n",
      "Epoch 29/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 268.4891 - mean_absolute_error: 13.7605 - val_loss: 130.4881 - val_mean_absolute_error: 9.9315\n",
      "Epoch 30/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 264.4534 - mean_absolute_error: 13.5770 - val_loss: 90.6190 - val_mean_absolute_error: 7.9455\n",
      "Epoch 31/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 238.2008 - mean_absolute_error: 12.6922 - val_loss: 99.7012 - val_mean_absolute_error: 8.5471\n",
      "Epoch 32/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 221.9488 - mean_absolute_error: 12.3910 - val_loss: 81.6352 - val_mean_absolute_error: 7.6612\n",
      "Epoch 33/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 194.8175 - mean_absolute_error: 11.5935 - val_loss: 42.9524 - val_mean_absolute_error: 5.4288\n",
      "Epoch 34/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 172.2758 - mean_absolute_error: 10.8945 - val_loss: 79.9657 - val_mean_absolute_error: 7.7395\n",
      "Epoch 35/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 150.7388 - mean_absolute_error: 10.0839 - val_loss: 61.4934 - val_mean_absolute_error: 6.7337\n",
      "Epoch 36/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 125.0562 - mean_absolute_error: 9.3018 - val_loss: 32.9587 - val_mean_absolute_error: 4.7468\n",
      "Epoch 37/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 101.8011 - mean_absolute_error: 8.1633 - val_loss: 23.8814 - val_mean_absolute_error: 3.9775\n",
      "Epoch 38/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 79.9685 - mean_absolute_error: 7.3911 - val_loss: 37.8287 - val_mean_absolute_error: 5.1081\n",
      "Epoch 39/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 56.4444 - mean_absolute_error: 6.0990 - val_loss: 68.9953 - val_mean_absolute_error: 7.4566\n",
      "Epoch 40/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 42.8072 - mean_absolute_error: 5.2976 - val_loss: 29.5314 - val_mean_absolute_error: 4.2718\n",
      "Epoch 41/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 26.3200 - mean_absolute_error: 3.9064 - val_loss: 18.1752 - val_mean_absolute_error: 3.3948\n",
      "Epoch 42/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 18.6683 - mean_absolute_error: 3.1743 - val_loss: 25.3294 - val_mean_absolute_error: 3.9121\n",
      "Epoch 43/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 15.1712 - mean_absolute_error: 2.9481 - val_loss: 20.7524 - val_mean_absolute_error: 3.5451\n",
      "Epoch 44/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 13.1537 - mean_absolute_error: 2.7954 - val_loss: 17.9877 - val_mean_absolute_error: 3.3780\n",
      "Epoch 45/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 12.3439 - mean_absolute_error: 2.7160 - val_loss: 21.4310 - val_mean_absolute_error: 3.6005\n",
      "Epoch 46/100\n",
      "9/9 [==============================] - 0s 16ms/step - loss: 11.4582 - mean_absolute_error: 2.5961 - val_loss: 24.1232 - val_mean_absolute_error: 3.7813\n",
      "Epoch 47/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 10.2679 - mean_absolute_error: 2.4267 - val_loss: 18.8550 - val_mean_absolute_error: 3.4302\n",
      "Epoch 48/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9.3842 - mean_absolute_error: 2.3220 - val_loss: 19.1599 - val_mean_absolute_error: 3.4536\n",
      "Epoch 49/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9.1129 - mean_absolute_error: 2.2912 - val_loss: 18.4533 - val_mean_absolute_error: 3.4017\n",
      "Epoch 50/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 8.7890 - mean_absolute_error: 2.2500 - val_loss: 18.7078 - val_mean_absolute_error: 3.4220\n",
      "Epoch 51/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 8.8217 - mean_absolute_error: 2.2680 - val_loss: 17.9961 - val_mean_absolute_error: 3.3924\n",
      "Epoch 52/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9.1651 - mean_absolute_error: 2.3361 - val_loss: 23.7591 - val_mean_absolute_error: 3.7634\n",
      "Epoch 53/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 9.9622 - mean_absolute_error: 2.4045 - val_loss: 18.7876 - val_mean_absolute_error: 3.4228\n",
      "Epoch 54/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9.4699 - mean_absolute_error: 2.3287 - val_loss: 17.7715 - val_mean_absolute_error: 3.5946\n",
      "Epoch 55/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9.7712 - mean_absolute_error: 2.4316 - val_loss: 19.9197 - val_mean_absolute_error: 3.5025\n",
      "Epoch 56/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 8.9146 - mean_absolute_error: 2.2897 - val_loss: 22.0482 - val_mean_absolute_error: 3.6214\n",
      "Epoch 57/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 8.8860 - mean_absolute_error: 2.2822 - val_loss: 17.2644 - val_mean_absolute_error: 3.3431\n",
      "Epoch 58/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.4488 - mean_absolute_error: 2.2180 - val_loss: 17.5037 - val_mean_absolute_error: 3.3775\n",
      "Epoch 59/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 8.6326 - mean_absolute_error: 2.2684 - val_loss: 21.4338 - val_mean_absolute_error: 3.5864\n",
      "Epoch 60/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9.1537 - mean_absolute_error: 2.3590 - val_loss: 17.3727 - val_mean_absolute_error: 3.3106\n",
      "Epoch 61/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.4921 - mean_absolute_error: 2.2415 - val_loss: 16.7473 - val_mean_absolute_error: 3.3244\n",
      "Epoch 62/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9.1707 - mean_absolute_error: 2.3308 - val_loss: 17.2109 - val_mean_absolute_error: 3.3930\n",
      "Epoch 63/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 10.3564 - mean_absolute_error: 2.5333 - val_loss: 16.7874 - val_mean_absolute_error: 3.3044\n",
      "Epoch 64/100\n",
      "9/9 [==============================] - 0s 16ms/step - loss: 8.2819 - mean_absolute_error: 2.2293 - val_loss: 20.4294 - val_mean_absolute_error: 3.5166\n",
      "Epoch 65/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.7183 - mean_absolute_error: 2.1365 - val_loss: 17.2444 - val_mean_absolute_error: 3.3089\n",
      "Epoch 66/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.1809 - mean_absolute_error: 2.1766 - val_loss: 18.0330 - val_mean_absolute_error: 3.3656\n",
      "Epoch 67/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 8.0463 - mean_absolute_error: 2.1506 - val_loss: 18.5482 - val_mean_absolute_error: 3.3924\n",
      "Epoch 68/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.7049 - mean_absolute_error: 2.1564 - val_loss: 17.2098 - val_mean_absolute_error: 3.3118\n",
      "Epoch 69/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.6794 - mean_absolute_error: 2.1172 - val_loss: 17.5390 - val_mean_absolute_error: 3.3265\n",
      "Epoch 70/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.6850 - mean_absolute_error: 2.1080 - val_loss: 16.5801 - val_mean_absolute_error: 3.3349\n",
      "Epoch 71/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.6415 - mean_absolute_error: 2.2839 - val_loss: 20.6996 - val_mean_absolute_error: 3.5336\n",
      "Epoch 72/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.5112 - mean_absolute_error: 2.2343 - val_loss: 19.9243 - val_mean_absolute_error: 3.4757\n",
      "Epoch 73/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.8505 - mean_absolute_error: 2.1545 - val_loss: 17.4146 - val_mean_absolute_error: 3.3118\n",
      "Epoch 74/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.7231 - mean_absolute_error: 2.1702 - val_loss: 19.3015 - val_mean_absolute_error: 3.4325\n",
      "Epoch 75/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.5447 - mean_absolute_error: 2.0830 - val_loss: 16.6257 - val_mean_absolute_error: 3.2857\n",
      "Epoch 76/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.4284 - mean_absolute_error: 2.0876 - val_loss: 16.3151 - val_mean_absolute_error: 3.2719\n",
      "Epoch 77/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.1794 - mean_absolute_error: 2.2194 - val_loss: 17.0201 - val_mean_absolute_error: 3.4978\n",
      "Epoch 78/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9.5082 - mean_absolute_error: 2.3529 - val_loss: 27.7324 - val_mean_absolute_error: 4.0841\n",
      "Epoch 79/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 11.3054 - mean_absolute_error: 2.6672 - val_loss: 25.6469 - val_mean_absolute_error: 3.9288\n",
      "Epoch 80/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 9.3868 - mean_absolute_error: 2.3559 - val_loss: 16.2346 - val_mean_absolute_error: 3.2551\n",
      "Epoch 81/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 8.3710 - mean_absolute_error: 2.2608 - val_loss: 16.5867 - val_mean_absolute_error: 3.4838\n",
      "Epoch 82/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.9727 - mean_absolute_error: 2.1992 - val_loss: 16.3824 - val_mean_absolute_error: 3.2400\n",
      "Epoch 83/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.6698 - mean_absolute_error: 2.1357 - val_loss: 17.3060 - val_mean_absolute_error: 3.3056\n",
      "Epoch 84/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 8.3975 - mean_absolute_error: 2.2569 - val_loss: 17.9779 - val_mean_absolute_error: 3.3329\n",
      "Epoch 85/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.5509 - mean_absolute_error: 2.0783 - val_loss: 17.3274 - val_mean_absolute_error: 3.3013\n",
      "Epoch 86/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.4735 - mean_absolute_error: 2.1229 - val_loss: 18.6924 - val_mean_absolute_error: 3.3815\n",
      "Epoch 87/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.6060 - mean_absolute_error: 2.0985 - val_loss: 16.3038 - val_mean_absolute_error: 3.2598\n",
      "Epoch 88/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.6580 - mean_absolute_error: 2.1542 - val_loss: 16.4043 - val_mean_absolute_error: 3.4695\n",
      "Epoch 89/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 10.1457 - mean_absolute_error: 2.5426 - val_loss: 22.0767 - val_mean_absolute_error: 3.6596\n",
      "Epoch 90/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 9.5535 - mean_absolute_error: 2.4172 - val_loss: 20.9411 - val_mean_absolute_error: 3.5762\n",
      "Epoch 91/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 10.3155 - mean_absolute_error: 2.6166 - val_loss: 16.6821 - val_mean_absolute_error: 3.2254\n",
      "Epoch 92/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 12.4483 - mean_absolute_error: 2.8270 - val_loss: 15.5504 - val_mean_absolute_error: 3.3217\n",
      "Epoch 93/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 10.5022 - mean_absolute_error: 2.5401 - val_loss: 17.9325 - val_mean_absolute_error: 3.7440\n",
      "Epoch 94/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 11.5015 - mean_absolute_error: 2.6302 - val_loss: 16.5672 - val_mean_absolute_error: 3.5366\n",
      "Epoch 95/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.7135 - mean_absolute_error: 2.1525 - val_loss: 16.2206 - val_mean_absolute_error: 3.2506\n",
      "Epoch 96/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.1146 - mean_absolute_error: 2.0605 - val_loss: 17.1092 - val_mean_absolute_error: 3.2700\n",
      "Epoch 97/100\n",
      "9/9 [==============================] - 0s 11ms/step - loss: 7.5871 - mean_absolute_error: 2.1096 - val_loss: 16.9750 - val_mean_absolute_error: 3.2592\n",
      "Epoch 98/100\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 7.3541 - mean_absolute_error: 2.0733 - val_loss: 17.1165 - val_mean_absolute_error: 3.2681\n",
      "Epoch 99/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.5984 - mean_absolute_error: 2.1033 - val_loss: 15.7645 - val_mean_absolute_error: 3.2406\n",
      "Epoch 100/100\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 7.2213 - mean_absolute_error: 2.0487 - val_loss: 15.7119 - val_mean_absolute_error: 3.1666\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x26aa9f77f70>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = Sequential([layers.Input((3, 1)),\n",
    "                    layers.LSTM(64),\n",
    "                    layers.Dense(32, activation='relu'),\n",
    "                    layers.Dense(32, activation='relu'),\n",
    "                    layers.Dense(1)])\n",
    "\n",
    "model.compile(loss='mse', \n",
    "              optimizer=Adam(learning_rate=0.001),\n",
    "              metrics=['mean_absolute_error'])\n",
    "\n",
    "model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ca2b7541",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 1s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26aae6b3c70>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_predictions = model.predict(X_train).flatten()\n",
    "\n",
    "plt.plot(dates_train, train_predictions)\n",
    "plt.plot(dates_train, y_train)\n",
    "plt.legend(['Training Predictions', 'Training Observations'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7c48d9bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26aad512d00>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_predictions = model.predict(X_val).flatten()\n",
    "\n",
    "plt.plot(dates_val, val_predictions)\n",
    "plt.plot(dates_val, y_val)\n",
    "plt.legend(['Validation Predictions', 'Validation Observations'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9c8648df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26aa9f865e0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(X_test).flatten()\n",
    "\n",
    "plt.plot(dates_test, test_predictions)\n",
    "plt.plot(dates_test, y_test)\n",
    "plt.legend(['Testing Predictions', 'Testing Observations'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d264c214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26a9b1e1a60>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dates_train, train_predictions)\n",
    "plt.plot(dates_train, y_train)\n",
    "plt.plot(dates_val, val_predictions)\n",
    "plt.plot(dates_val, y_val)\n",
    "plt.plot(dates_test, test_predictions)\n",
    "plt.plot(dates_test, y_test)\n",
    "plt.legend(['Training Predictions', \n",
    "            'Training Observations',\n",
    "            'Validation Predictions', \n",
    "            'Validation Observations',\n",
    "            'Testing Predictions', \n",
    "            'Testing Observations'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "49397a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 31ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 33ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 30ms/step\n",
      "1/1 [==============================] - 0s 32ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 26ms/step\n",
      "1/1 [==============================] - 0s 27ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 29ms/step\n",
      "1/1 [==============================] - 0s 41ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n",
      "1/1 [==============================] - 0s 40ms/step\n",
      "1/1 [==============================] - 0s 28ms/step\n"
     ]
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "recursive_predictions = []\n",
    "recursive_dates = dates_test[0:]\n",
    "last_window  = X_test[0]\n",
    "\n",
    "for target_date in recursive_dates:\n",
    "    next_prediction = model.predict(np.array([last_window])).flatten()\n",
    "    recursive_predictions.append(next_prediction)\n",
    "    new_window = list(last_window[1:])\n",
    "    new_window.append(next_prediction)\n",
    "    new_window = np.array(new_window)\n",
    "    last_window = new_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bae12751",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26aae6e9c70>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dates_test, y_test)\n",
    "plt.plot(recursive_dates, recursive_predictions)\n",
    "plt.legend([\n",
    "            'Testing Observations',\n",
    "            'Recursive Predictions'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}