forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomass_history_unscaled = pd.read_csv(r\"C:\\Users\\parul\\OneDrive\\Desktop\\Projects\\ShellAI\\Shell_AI_Hackathon\\dataset\\Biomass_History.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.33144</td>\n",
       "      <td>8.475744</td>\n",
       "      <td>8.868568</td>\n",
       "      <td>9.202181</td>\n",
       "      <td>6.023070</td>\n",
       "      <td>10.788374</td>\n",
       "      <td>6.647325</td>\n",
       "      <td>7.387925</td>\n",
       "      <td>5.180296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.41106</td>\n",
       "      <td>24.029778</td>\n",
       "      <td>28.551348</td>\n",
       "      <td>25.866415</td>\n",
       "      <td>21.634459</td>\n",
       "      <td>34.419411</td>\n",
       "      <td>27.361908</td>\n",
       "      <td>40.431847</td>\n",
       "      <td>42.126945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.49069</td>\n",
       "      <td>44.831635</td>\n",
       "      <td>66.111168</td>\n",
       "      <td>56.982258</td>\n",
       "      <td>53.003735</td>\n",
       "      <td>70.917908</td>\n",
       "      <td>42.517117</td>\n",
       "      <td>59.181629</td>\n",
       "      <td>73.203232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.57031</td>\n",
       "      <td>59.974419</td>\n",
       "      <td>80.821304</td>\n",
       "      <td>78.956543</td>\n",
       "      <td>63.160561</td>\n",
       "      <td>93.513924</td>\n",
       "      <td>70.203171</td>\n",
       "      <td>74.536720</td>\n",
       "      <td>101.067352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.64994</td>\n",
       "      <td>14.653370</td>\n",
       "      <td>19.327524</td>\n",
       "      <td>21.928144</td>\n",
       "      <td>17.899586</td>\n",
       "      <td>19.534035</td>\n",
       "      <td>19.165791</td>\n",
       "      <td>16.531315</td>\n",
       "      <td>26.086885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index  Latitude  Longitude       2010       2011       2012       2013  \\\n",
       "0      0  24.66818   71.33144   8.475744   8.868568   9.202181   6.023070   \n",
       "1      1  24.66818   71.41106  24.029778  28.551348  25.866415  21.634459   \n",
       "2      2  24.66818   71.49069  44.831635  66.111168  56.982258  53.003735   \n",
       "3      3  24.66818   71.57031  59.974419  80.821304  78.956543  63.160561   \n",
       "4      4  24.66818   71.64994  14.653370  19.327524  21.928144  17.899586   \n",
       "\n",
       "        2014       2015       2016        2017  \n",
       "0  10.788374   6.647325   7.387925    5.180296  \n",
       "1  34.419411  27.361908  40.431847   42.126945  \n",
       "2  70.917908  42.517117  59.181629   73.203232  \n",
       "3  93.513924  70.203171  74.536720  101.067352  \n",
       "4  19.534035  19.165791  16.531315   26.086885  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_history_unscaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomass_history_scaled = pd.read_csv(r\"C:\\Users\\parul\\OneDrive\\Desktop\\Projects\\ShellAI\\Shell_AI_Hackathon\\preprocessed\\scaled.csv\").drop([\"Index\", \"Unnamed: 0\"], axis = 1)\n",
    "biomass_history_unscaled = biomass_history_unscaled.drop([\"Index\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.33144</td>\n",
       "      <td>0.009591</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.010555</td>\n",
       "      <td>0.004885</td>\n",
       "      <td>0.012375</td>\n",
       "      <td>0.008621</td>\n",
       "      <td>0.014369</td>\n",
       "      <td>0.005862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.41106</td>\n",
       "      <td>0.027192</td>\n",
       "      <td>0.037028</td>\n",
       "      <td>0.029670</td>\n",
       "      <td>0.017548</td>\n",
       "      <td>0.039481</td>\n",
       "      <td>0.035486</td>\n",
       "      <td>0.078638</td>\n",
       "      <td>0.047671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.49069</td>\n",
       "      <td>0.050732</td>\n",
       "      <td>0.085740</td>\n",
       "      <td>0.065361</td>\n",
       "      <td>0.042992</td>\n",
       "      <td>0.081346</td>\n",
       "      <td>0.055141</td>\n",
       "      <td>0.115106</td>\n",
       "      <td>0.082838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.57031</td>\n",
       "      <td>0.067868</td>\n",
       "      <td>0.104818</td>\n",
       "      <td>0.090567</td>\n",
       "      <td>0.051231</td>\n",
       "      <td>0.107265</td>\n",
       "      <td>0.091047</td>\n",
       "      <td>0.144971</td>\n",
       "      <td>0.114369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.64994</td>\n",
       "      <td>0.016582</td>\n",
       "      <td>0.025066</td>\n",
       "      <td>0.025152</td>\n",
       "      <td>0.014518</td>\n",
       "      <td>0.022406</td>\n",
       "      <td>0.024856</td>\n",
       "      <td>0.032152</td>\n",
       "      <td>0.029520</td>\n",
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      "text/plain": [
       "   Latitude  Longitude      2010      2011      2012      2013      2014  \\\n",
       "0  24.66818   71.33144  0.009591  0.011501  0.010555  0.004885  0.012375   \n",
       "1  24.66818   71.41106  0.027192  0.037028  0.029670  0.017548  0.039481   \n",
       "2  24.66818   71.49069  0.050732  0.085740  0.065361  0.042992  0.081346   \n",
       "3  24.66818   71.57031  0.067868  0.104818  0.090567  0.051231  0.107265   \n",
       "4  24.66818   71.64994  0.016582  0.025066  0.025152  0.014518  0.022406   \n",
       "\n",
       "       2015      2016      2017  \n",
       "0  0.008621  0.014369  0.005862  \n",
       "1  0.035486  0.078638  0.047671  \n",
       "2  0.055141  0.115106  0.082838  \n",
       "3  0.091047  0.144971  0.114369  \n",
       "4  0.024856  0.032152  0.029520  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_history_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.33144</td>\n",
       "      <td>8.475744</td>\n",
       "      <td>8.868568</td>\n",
       "      <td>9.202181</td>\n",
       "      <td>6.023070</td>\n",
       "      <td>10.788374</td>\n",
       "      <td>6.647325</td>\n",
       "      <td>7.387925</td>\n",
       "      <td>5.180296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.41106</td>\n",
       "      <td>24.029778</td>\n",
       "      <td>28.551348</td>\n",
       "      <td>25.866415</td>\n",
       "      <td>21.634459</td>\n",
       "      <td>34.419411</td>\n",
       "      <td>27.361908</td>\n",
       "      <td>40.431847</td>\n",
       "      <td>42.126945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.49069</td>\n",
       "      <td>44.831635</td>\n",
       "      <td>66.111168</td>\n",
       "      <td>56.982258</td>\n",
       "      <td>53.003735</td>\n",
       "      <td>70.917908</td>\n",
       "      <td>42.517117</td>\n",
       "      <td>59.181629</td>\n",
       "      <td>73.203232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.57031</td>\n",
       "      <td>59.974419</td>\n",
       "      <td>80.821304</td>\n",
       "      <td>78.956543</td>\n",
       "      <td>63.160561</td>\n",
       "      <td>93.513924</td>\n",
       "      <td>70.203171</td>\n",
       "      <td>74.536720</td>\n",
       "      <td>101.067352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.64994</td>\n",
       "      <td>14.653370</td>\n",
       "      <td>19.327524</td>\n",
       "      <td>21.928144</td>\n",
       "      <td>17.899586</td>\n",
       "      <td>19.534035</td>\n",
       "      <td>19.165791</td>\n",
       "      <td>16.531315</td>\n",
       "      <td>26.086885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Latitude  Longitude       2010       2011       2012       2013       2014  \\\n",
       "0  24.66818   71.33144   8.475744   8.868568   9.202181   6.023070  10.788374   \n",
       "1  24.66818   71.41106  24.029778  28.551348  25.866415  21.634459  34.419411   \n",
       "2  24.66818   71.49069  44.831635  66.111168  56.982258  53.003735  70.917908   \n",
       "3  24.66818   71.57031  59.974419  80.821304  78.956543  63.160561  93.513924   \n",
       "4  24.66818   71.64994  14.653370  19.327524  21.928144  17.899586  19.534035   \n",
       "\n",
       "        2015       2016        2017  \n",
       "0   6.647325   7.387925    5.180296  \n",
       "1  27.361908  40.431847   42.126945  \n",
       "2  42.517117  59.181629   73.203232  \n",
       "3  70.203171  74.536720  101.067352  \n",
       "4  19.165791  16.531315   26.086885  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_history_unscaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "location = biomass_history_scaled[[\"Latitude\", \"Longitude\"]]\n",
    "time_series_data_scaled = biomass_history_scaled.iloc[:,2:9]\n",
    "y_time_series_data_scaled = biomass_history_scaled[[\"2017\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_series_data_unscaled = biomass_history_unscaled.iloc[:,2:9]\n",
    "y_time_series_data_unscaled = biomass_history_unscaled[[\"2017\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009591</td>\n",
       "      <td>0.011501</td>\n",
       "      <td>0.010555</td>\n",
       "      <td>0.004885</td>\n",
       "      <td>0.012375</td>\n",
       "      <td>0.008621</td>\n",
       "      <td>0.014369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027192</td>\n",
       "      <td>0.037028</td>\n",
       "      <td>0.029670</td>\n",
       "      <td>0.017548</td>\n",
       "      <td>0.039481</td>\n",
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       "      <td>0.078638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.050732</td>\n",
       "      <td>0.085740</td>\n",
       "      <td>0.065361</td>\n",
       "      <td>0.042992</td>\n",
       "      <td>0.081346</td>\n",
       "      <td>0.055141</td>\n",
       "      <td>0.115106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.067868</td>\n",
       "      <td>0.104818</td>\n",
       "      <td>0.090567</td>\n",
       "      <td>0.051231</td>\n",
       "      <td>0.107265</td>\n",
       "      <td>0.091047</td>\n",
       "      <td>0.144971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.016582</td>\n",
       "      <td>0.025066</td>\n",
       "      <td>0.025152</td>\n",
       "      <td>0.014518</td>\n",
       "      <td>0.022406</td>\n",
       "      <td>0.024856</td>\n",
       "      <td>0.032152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2010      2011      2012      2013      2014      2015      2016\n",
       "0  0.009591  0.011501  0.010555  0.004885  0.012375  0.008621  0.014369\n",
       "1  0.027192  0.037028  0.029670  0.017548  0.039481  0.035486  0.078638\n",
       "2  0.050732  0.085740  0.065361  0.042992  0.081346  0.055141  0.115106\n",
       "3  0.067868  0.104818  0.090567  0.051231  0.107265  0.091047  0.144971\n",
       "4  0.016582  0.025066  0.025152  0.014518  0.022406  0.024856  0.032152"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series_data_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.475744</td>\n",
       "      <td>8.868568</td>\n",
       "      <td>9.202181</td>\n",
       "      <td>6.023070</td>\n",
       "      <td>10.788374</td>\n",
       "      <td>6.647325</td>\n",
       "      <td>7.387925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.029778</td>\n",
       "      <td>28.551348</td>\n",
       "      <td>25.866415</td>\n",
       "      <td>21.634459</td>\n",
       "      <td>34.419411</td>\n",
       "      <td>27.361908</td>\n",
       "      <td>40.431847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.831635</td>\n",
       "      <td>66.111168</td>\n",
       "      <td>56.982258</td>\n",
       "      <td>53.003735</td>\n",
       "      <td>70.917908</td>\n",
       "      <td>42.517117</td>\n",
       "      <td>59.181629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.974419</td>\n",
       "      <td>80.821304</td>\n",
       "      <td>78.956543</td>\n",
       "      <td>63.160561</td>\n",
       "      <td>93.513924</td>\n",
       "      <td>70.203171</td>\n",
       "      <td>74.536720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.653370</td>\n",
       "      <td>19.327524</td>\n",
       "      <td>21.928144</td>\n",
       "      <td>17.899586</td>\n",
       "      <td>19.534035</td>\n",
       "      <td>19.165791</td>\n",
       "      <td>16.531315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2010       2011       2012       2013       2014       2015       2016\n",
       "0   8.475744   8.868568   9.202181   6.023070  10.788374   6.647325   7.387925\n",
       "1  24.029778  28.551348  25.866415  21.634459  34.419411  27.361908  40.431847\n",
       "2  44.831635  66.111168  56.982258  53.003735  70.917908  42.517117  59.181629\n",
       "3  59.974419  80.821304  78.956543  63.160561  93.513924  70.203171  74.536720\n",
       "4  14.653370  19.327524  21.928144  17.899586  19.534035  19.165791  16.531315"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series_data_unscaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the years from the column names\n",
    "years = biomass_history_scaled.columns[2:9]\n",
    "i = 0\n",
    "# Loop through each row and plot the values against years\n",
    "for index, row in biomass_history_scaled.iterrows():\n",
    "    plt.plot(years, row[2:9])\n",
    "    i += 1\n",
    "    if i == 6:\n",
    "        break\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Data Plot of scaled datapoints')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the years from the column names\n",
    "years = biomass_history_unscaled.columns[2:9]\n",
    "i = 0\n",
    "# Loop through each row and plot the values against years\n",
    "for index, row in biomass_history_unscaled.iterrows():\n",
    "    plt.plot(years, row[2:9])\n",
    "    i += 1\n",
    "    if i == 6:\n",
    "        break\n",
    "\n",
    "# Customize the plot\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Data Plot of unscaled datapoints')\n",
    "plt.legend()\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test for stationary points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_scaled = time_series_data_scaled.iloc[:5,:]\n",
    "test_unscaled = time_series_data_unscaled.iloc[:5,:]\n",
    "y_scaled = y_time_series_data_scaled[:5]\n",
    "y_unscaled = y_time_series_data_unscaled[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to return if the point is stationary or not\n",
    "\n",
    "def ad_test(dataset):\n",
    "    dftest = adfuller(dataset, autolag = \"AIC\")\n",
    "    #print(dftest)\n",
    "    \n",
    "    if dftest[1] < 0.05:\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.475744</td>\n",
       "      <td>8.868568</td>\n",
       "      <td>9.202181</td>\n",
       "      <td>6.023070</td>\n",
       "      <td>10.788374</td>\n",
       "      <td>6.647325</td>\n",
       "      <td>7.387925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.029778</td>\n",
       "      <td>28.551348</td>\n",
       "      <td>25.866415</td>\n",
       "      <td>21.634459</td>\n",
       "      <td>34.419411</td>\n",
       "      <td>27.361908</td>\n",
       "      <td>40.431847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.831635</td>\n",
       "      <td>66.111168</td>\n",
       "      <td>56.982258</td>\n",
       "      <td>53.003735</td>\n",
       "      <td>70.917908</td>\n",
       "      <td>42.517117</td>\n",
       "      <td>59.181629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.974419</td>\n",
       "      <td>80.821304</td>\n",
       "      <td>78.956543</td>\n",
       "      <td>63.160561</td>\n",
       "      <td>93.513924</td>\n",
       "      <td>70.203171</td>\n",
       "      <td>74.536720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.653370</td>\n",
       "      <td>19.327524</td>\n",
       "      <td>21.928144</td>\n",
       "      <td>17.899586</td>\n",
       "      <td>19.534035</td>\n",
       "      <td>19.165791</td>\n",
       "      <td>16.531315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2010       2011       2012       2013       2014       2015       2016\n",
       "0   8.475744   8.868568   9.202181   6.023070  10.788374   6.647325   7.387925\n",
       "1  24.029778  28.551348  25.866415  21.634459  34.419411  27.361908  40.431847\n",
       "2  44.831635  66.111168  56.982258  53.003735  70.917908  42.517117  59.181629\n",
       "3  59.974419  80.821304  78.956543  63.160561  93.513924  70.203171  74.536720\n",
       "4  14.653370  19.327524  21.928144  17.899586  19.534035  19.165791  16.531315"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_unscaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformations to transform non-stationary point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1152"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to count number of stationary points, given a dataframe\n",
    "def number_of_non_stationary_points(dataframe):\n",
    "    count_of_non_stationary_points = 0\n",
    "    for row in dataframe.itertuples():\n",
    "\n",
    "        data = np.cbrt(pd.DataFrame(row[1:])).diff().dropna()\n",
    "\n",
    "        ad_test_result = ad_test(data)\n",
    "\n",
    "        if ad_test_result == True:\n",
    "            pass\n",
    "        else:\n",
    "            count_of_non_stationary_points += 1\n",
    "\n",
    "    return count_of_non_stationary_points\n",
    "\n",
    "number_of_non_stationary_points(time_series_data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to count number of stationary points using boxcox transformation, given a dataframe\n",
    "from scipy import stats\n",
    "def number_of_non_stationary_points_using_boxcox(dataframe):\n",
    "    count_of_non_stationary_points = 0\n",
    "\n",
    "    for row in dataframe.itertuples():\n",
    "        fitted_data, fitted_lambda = stats.boxcox(np.array(row[1:]))\n",
    "        ad_test_result = ad_test(fitted_data)\n",
    "        if ad_test_result == True:\n",
    "            pass\n",
    "        else:\n",
    "            count_of_non_stationary_points += 1\n",
    "\n",
    "    return count_of_non_stationary_points\n",
    "\n",
    "number_of_non_stationary_points_using_boxcox(test_unscaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming data using trnsformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_data(data):\n",
    "\n",
    "    transformed_data = np.cbrt(data)\n",
    "\n",
    "    return transformed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = transform_data(time_series_data_scaled.T).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_transformed = np.cbrt(y_time_series_data_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, y_transformed, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>0.127068</td>\n",
       "      <td>0.103245</td>\n",
       "      <td>0.132100</td>\n",
       "      <td>0.138414</td>\n",
       "      <td>0.104979</td>\n",
       "      <td>0.126741</td>\n",
       "      <td>0.166999</td>\n",
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       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>0.850313</td>\n",
       "      <td>0.915326</td>\n",
       "      <td>0.620706</td>\n",
       "      <td>0.767783</td>\n",
       "      <td>0.828615</td>\n",
       "      <td>0.547813</td>\n",
       "      <td>0.470929</td>\n",
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       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>0.717739</td>\n",
       "      <td>0.728700</td>\n",
       "      <td>0.636708</td>\n",
       "      <td>0.621759</td>\n",
       "      <td>0.660992</td>\n",
       "      <td>0.756545</td>\n",
       "      <td>0.784400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.376874</td>\n",
       "      <td>0.444373</td>\n",
       "      <td>0.372621</td>\n",
       "      <td>0.453121</td>\n",
       "      <td>0.627149</td>\n",
       "      <td>0.465827</td>\n",
       "      <td>0.536190</td>\n",
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       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>0.595020</td>\n",
       "      <td>0.601419</td>\n",
       "      <td>0.611304</td>\n",
       "      <td>0.544106</td>\n",
       "      <td>0.668349</td>\n",
       "      <td>0.661935</td>\n",
       "      <td>0.780859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>0.509848</td>\n",
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       "      <td>0.554844</td>\n",
       "      <td>0.580223</td>\n",
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       "      <td>0.632268</td>\n",
       "      <td>0.727084</td>\n",
       "      <td>0.353586</td>\n",
       "      <td>0.662993</td>\n",
       "      <td>0.694735</td>\n",
       "      <td>0.673099</td>\n",
       "      <td>0.523792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>0.117917</td>\n",
       "      <td>0.124371</td>\n",
       "      <td>0.111089</td>\n",
       "      <td>0.130966</td>\n",
       "      <td>0.125228</td>\n",
       "      <td>0.138433</td>\n",
       "      <td>0.153496</td>\n",
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       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>0.841954</td>\n",
       "      <td>0.849925</td>\n",
       "      <td>0.852825</td>\n",
       "      <td>0.759793</td>\n",
       "      <td>0.852825</td>\n",
       "      <td>0.849925</td>\n",
       "      <td>0.842700</td>\n",
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       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>0.472997</td>\n",
       "      <td>0.551184</td>\n",
       "      <td>0.567356</td>\n",
       "      <td>0.420086</td>\n",
       "      <td>0.506220</td>\n",
       "      <td>0.515536</td>\n",
       "      <td>0.690321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>484 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          2010      2011      2012      2013      2014      2015      2016\n",
       "2328  0.127068  0.103245  0.132100  0.138414  0.104979  0.126741  0.166999\n",
       "1742  0.850313  0.915326  0.620706  0.767783  0.828615  0.547813  0.470929\n",
       "810   0.717739  0.728700  0.636708  0.621759  0.660992  0.756545  0.784400\n",
       "2014  0.376874  0.444373  0.372621  0.453121  0.627149  0.465827  0.536190\n",
       "618   0.595020  0.601419  0.611304  0.544106  0.668349  0.661935  0.780859\n",
       "...        ...       ...       ...       ...       ...       ...       ...\n",
       "1814  0.616515  0.632773  0.509848  0.536349  0.554844  0.580223  0.638425\n",
       "1388  0.632268  0.727084  0.353586  0.662993  0.694735  0.673099  0.523792\n",
       "2270  0.117917  0.124371  0.111089  0.130966  0.125228  0.138433  0.153496\n",
       "794   0.841954  0.849925  0.852825  0.759793  0.852825  0.849925  0.842700\n",
       "682   0.472997  0.551184  0.567356  0.420086  0.506220  0.515536  0.690321\n",
       "\n",
       "[484 rows x 7 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implementing Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9586395339423832"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>386</th>\n",
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       "      <td>0.522669</td>\n",
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       "      <td>0.175782</td>\n",
       "      <td>0.191031</td>\n",
       "      <td>0.116379</td>\n",
       "      <td>0.241361</td>\n",
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       "      <th>459</th>\n",
       "      <td>0.149928</td>\n",
       "      <td>0.151597</td>\n",
       "      <td>0.233746</td>\n",
       "      <td>0.161700</td>\n",
       "      <td>0.137714</td>\n",
       "      <td>0.149907</td>\n",
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       "      <th>1129</th>\n",
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       "      <td>0.452615</td>\n",
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       "      <th>1914</th>\n",
       "      <td>0.874117</td>\n",
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       "      <td>0.576836</td>\n",
       "      <td>0.927572</td>\n",
       "      <td>0.776907</td>\n",
       "      <td>0.902602</td>\n",
       "      <td>0.844643</td>\n",
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       "      <th>1033</th>\n",
       "      <td>0.280019</td>\n",
       "      <td>0.352983</td>\n",
       "      <td>0.173652</td>\n",
       "      <td>0.273024</td>\n",
       "      <td>0.324438</td>\n",
       "      <td>0.307431</td>\n",
       "      <td>0.256275</td>\n",
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       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>0.254928</td>\n",
       "      <td>0.343291</td>\n",
       "      <td>0.243417</td>\n",
       "      <td>0.254451</td>\n",
       "      <td>0.370744</td>\n",
       "      <td>0.277632</td>\n",
       "      <td>0.303338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.438618</td>\n",
       "      <td>0.471913</td>\n",
       "      <td>0.433578</td>\n",
       "      <td>0.386280</td>\n",
       "      <td>0.433578</td>\n",
       "      <td>0.471913</td>\n",
       "      <td>0.583882</td>\n",
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       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>0.242402</td>\n",
       "      <td>0.236302</td>\n",
       "      <td>0.239969</td>\n",
       "      <td>0.207970</td>\n",
       "      <td>0.213700</td>\n",
       "      <td>0.258527</td>\n",
       "      <td>0.303241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>0.838954</td>\n",
       "      <td>0.851484</td>\n",
       "      <td>0.659939</td>\n",
       "      <td>0.720116</td>\n",
       "      <td>0.745151</td>\n",
       "      <td>0.758932</td>\n",
       "      <td>0.859352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1934 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          2010      2011      2012      2013      2014      2015      2016\n",
       "386   0.603261  0.686457  0.606268  0.522669  0.619570  0.627201  0.693207\n",
       "1735  0.167046  0.150918  0.137734  0.175782  0.191031  0.116379  0.241361\n",
       "459   0.149928  0.151597  0.233746  0.161700  0.137714  0.149907  0.304149\n",
       "1129  0.403375  0.412289  0.390379  0.363824  0.447626  0.452615  0.519776\n",
       "1914  0.874117  0.849048  0.576836  0.927572  0.776907  0.902602  0.844643\n",
       "...        ...       ...       ...       ...       ...       ...       ...\n",
       "1033  0.280019  0.352983  0.173652  0.273024  0.324438  0.307431  0.256275\n",
       "1731  0.254928  0.343291  0.243417  0.254451  0.370744  0.277632  0.303338\n",
       "763   0.438618  0.471913  0.433578  0.386280  0.433578  0.471913  0.583882\n",
       "835   0.242402  0.236302  0.239969  0.207970  0.213700  0.258527  0.303241\n",
       "1653  0.838954  0.851484  0.659939  0.720116  0.745151  0.758932  0.859352\n",
       "\n",
       "[1934 rows x 7 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>0.560791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1735</th>\n",
       "      <td>0.187360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>0.156794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>0.465988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>0.783193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>0.367292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>0.259489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.438618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>835</th>\n",
       "      <td>0.233280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>0.819384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1934 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          2017\n",
       "386   0.560791\n",
       "1735  0.187360\n",
       "459   0.156794\n",
       "1129  0.465988\n",
       "1914  0.783193\n",
       "...        ...\n",
       "1033  0.367292\n",
       "1731  0.259489\n",
       "763   0.438618\n",
       "835   0.233280\n",
       "1653  0.819384\n",
       "\n",
       "[1934 rows x 1 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm_3 (LSTM)               (None, 7, 100)            40800     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 7, 100)            0         \n",
      "                                                                 \n",
      " lstm_4 (LSTM)               (None, 7, 100)            80400     \n",
      "                                                                 \n",
      " dropout_4 (Dropout)         (None, 7, 100)            0         \n",
      "                                                                 \n",
      " lstm_5 (LSTM)               (None, 100)               80400     \n",
      "                                                                 \n",
      " dropout_5 (Dropout)         (None, 100)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 201701 (787.89 KB)\n",
      "Trainable params: 201701 (787.89 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# setting the seed to achieve consistent and less random predictions at each execution\n",
    "np.random.seed(0)\n",
    "\n",
    "# setting the model architecture\n",
    "model=Sequential()\n",
    "model.add(LSTM(100,return_sequences = True, activation='tanh',input_shape=(len(X_train.columns),1)))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(LSTM(100,return_sequences = True, activation='tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "\n",
    "model.add(LSTM(100, activation = 'tanh'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# printing the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "158/158 [==============================] - 13s 39ms/step - loss: 0.0167 - val_loss: 0.0027\n",
      "Epoch 2/50\n",
      "158/158 [==============================] - 5s 31ms/step - loss: 0.0049 - val_loss: 0.0030\n",
      "Epoch 3/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0043 - val_loss: 0.0029\n",
      "Epoch 4/50\n",
      "158/158 [==============================] - 4s 26ms/step - loss: 0.0045 - val_loss: 0.0032\n",
      "Epoch 5/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0045 - val_loss: 0.0029\n",
      "Epoch 6/50\n",
      "158/158 [==============================] - 4s 25ms/step - loss: 0.0044 - val_loss: 0.0040\n",
      "Epoch 7/50\n",
      "158/158 [==============================] - 5s 32ms/step - loss: 0.0049 - val_loss: 0.0030\n",
      "Epoch 8/50\n",
      "158/158 [==============================] - 5s 33ms/step - loss: 0.0049 - val_loss: 0.0026\n",
      "Epoch 9/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0040 - val_loss: 0.0028\n",
      "Epoch 10/50\n",
      "158/158 [==============================] - 5s 30ms/step - loss: 0.0039 - val_loss: 0.0035\n",
      "Epoch 11/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0039 - val_loss: 0.0026\n",
      "Epoch 12/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0040 - val_loss: 0.0026\n",
      "Epoch 13/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0040 - val_loss: 0.0024\n",
      "Epoch 14/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0039 - val_loss: 0.0026\n",
      "Epoch 15/50\n",
      "158/158 [==============================] - 5s 31ms/step - loss: 0.0042 - val_loss: 0.0027\n",
      "Epoch 16/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0041 - val_loss: 0.0050\n",
      "Epoch 17/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0041 - val_loss: 0.0030\n",
      "Epoch 18/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0037 - val_loss: 0.0028\n",
      "Epoch 19/50\n",
      "158/158 [==============================] - 5s 30ms/step - loss: 0.0039 - val_loss: 0.0027\n",
      "Epoch 20/50\n",
      "158/158 [==============================] - 5s 29ms/step - loss: 0.0037 - val_loss: 0.0030\n",
      "Epoch 21/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0039 - val_loss: 0.0025\n",
      "Epoch 22/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0038 - val_loss: 0.0027\n",
      "Epoch 23/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0038 - val_loss: 0.0027\n",
      "Epoch 24/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0039 - val_loss: 0.0040\n",
      "Epoch 25/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0037 - val_loss: 0.0039\n",
      "Epoch 26/50\n",
      "158/158 [==============================] - 5s 30ms/step - loss: 0.0043 - val_loss: 0.0046\n",
      "Epoch 27/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0038 - val_loss: 0.0029\n",
      "Epoch 28/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0038 - val_loss: 0.0029\n",
      "Epoch 29/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0035 - val_loss: 0.0035\n",
      "Epoch 30/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0038 - val_loss: 0.0029\n",
      "Epoch 31/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0037 - val_loss: 0.0027\n",
      "Epoch 32/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 33/50\n",
      "158/158 [==============================] - 5s 30ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 34/50\n",
      "158/158 [==============================] - 4s 26ms/step - loss: 0.0035 - val_loss: 0.0035\n",
      "Epoch 35/50\n",
      "158/158 [==============================] - 5s 29ms/step - loss: 0.0036 - val_loss: 0.0038\n",
      "Epoch 36/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 37/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0036 - val_loss: 0.0027\n",
      "Epoch 38/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0034 - val_loss: 0.0026\n",
      "Epoch 39/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0034 - val_loss: 0.0028\n",
      "Epoch 40/50\n",
      "158/158 [==============================] - 5s 29ms/step - loss: 0.0034 - val_loss: 0.0023\n",
      "Epoch 41/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0035 - val_loss: 0.0027\n",
      "Epoch 42/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0034 - val_loss: 0.0024\n",
      "Epoch 43/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0033 - val_loss: 0.0023\n",
      "Epoch 44/50\n",
      "158/158 [==============================] - 4s 27ms/step - loss: 0.0030 - val_loss: 0.0024\n",
      "Epoch 45/50\n",
      "158/158 [==============================] - 5s 30ms/step - loss: 0.0029 - val_loss: 0.0019\n",
      "Epoch 46/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0027 - val_loss: 0.0021\n",
      "Epoch 47/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0026 - val_loss: 0.0020\n",
      "Epoch 48/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0026 - val_loss: 0.0020\n",
      "Epoch 49/50\n",
      "158/158 [==============================] - 5s 29ms/step - loss: 0.0024 - val_loss: 0.0020\n",
      "Epoch 50/50\n",
      "158/158 [==============================] - 4s 28ms/step - loss: 0.0023 - val_loss: 0.0025\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1a75fa23ac0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compiling the model\n",
    "model.compile(loss='mse' , optimizer='adam')\n",
    "\n",
    "# fitting the model using the training dataset\n",
    "model.fit(X_train, y_train, validation_split=0.35, epochs=50, batch_size=8, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61/61 [==============================] - 2s 16ms/step\n"
     ]
    }
   ],
   "source": [
    "# Predictions\n",
    "predictions = model.predict(X_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6165709 ],\n",
       "       [0.21573557],\n",
       "       [0.1918622 ],\n",
       "       ...,\n",
       "       [0.4513561 ],\n",
       "       [0.27134755],\n",
       "       [0.82177025]], dtype=float32)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   1/1934 [..............................] - ETA: 3:20 - loss: 0.0031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1934/1934 [==============================] - 11s 6ms/step - loss: 0.0025\n",
      "484/484 [==============================] - 3s 6ms/step - loss: 0.0024\n",
      "Train Loss = 0.0025\n",
      "Test Loss = 0.0024\n"
     ]
    }
   ],
   "source": [
    "# calculating the training mean-squared-error\n",
    "train_loss = model.evaluate(X_train, y_train, batch_size = 1)\n",
    "\n",
    "# calculating the test mean-squared-error\n",
    "test_loss = model.evaluate(X_test, y_test, batch_size = 1)\n",
    "\n",
    "# printing the training and the test mean-squared-errors\n",
    "print('Train Loss =', round(train_loss,4))\n",
    "print('Test Loss =', round(test_loss,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "197/197 [==============================] - 5s 25ms/step - loss: 0.0020 - val_loss: 0.0056\n",
      "Epoch 2/50\n",
      "197/197 [==============================] - 5s 26ms/step - loss: 0.0017 - val_loss: 0.0041\n",
      "Epoch 3/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0018 - val_loss: 0.0043\n",
      "Epoch 4/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0018 - val_loss: 0.0056\n",
      "Epoch 5/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0018 - val_loss: 0.0059\n",
      "Epoch 6/50\n",
      "197/197 [==============================] - 6s 30ms/step - loss: 0.0017 - val_loss: 0.0043\n",
      "Epoch 7/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0018 - val_loss: 0.0040\n",
      "Epoch 8/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0017 - val_loss: 0.0062\n",
      "Epoch 9/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0017 - val_loss: 0.0041\n",
      "Epoch 10/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0017 - val_loss: 0.0047\n",
      "Epoch 11/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0017 - val_loss: 0.0045\n",
      "Epoch 12/50\n",
      "197/197 [==============================] - 5s 26ms/step - loss: 0.0018 - val_loss: 0.0039\n",
      "Epoch 13/50\n",
      "197/197 [==============================] - 6s 29ms/step - loss: 0.0016 - val_loss: 0.0047\n",
      "Epoch 14/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0017 - val_loss: 0.0048\n",
      "Epoch 15/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0017 - val_loss: 0.0067\n",
      "Epoch 16/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0015 - val_loss: 0.0061\n",
      "Epoch 17/50\n",
      "197/197 [==============================] - 6s 30ms/step - loss: 0.0016 - val_loss: 0.0055\n",
      "Epoch 18/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0015 - val_loss: 0.0046\n",
      "Epoch 19/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0015 - val_loss: 0.0046\n",
      "Epoch 20/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0016 - val_loss: 0.0041\n",
      "Epoch 21/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0016 - val_loss: 0.0032\n",
      "Epoch 22/50\n",
      "197/197 [==============================] - 6s 29ms/step - loss: 0.0016 - val_loss: 0.0038\n",
      "Epoch 23/50\n",
      "197/197 [==============================] - 6s 28ms/step - loss: 0.0016 - val_loss: 0.0042\n",
      "Epoch 24/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0015 - val_loss: 0.0046\n",
      "Epoch 25/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0016 - val_loss: 0.0053\n",
      "Epoch 26/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0015 - val_loss: 0.0046\n",
      "Epoch 27/50\n",
      "197/197 [==============================] - 6s 29ms/step - loss: 0.0015 - val_loss: 0.0041\n",
      "Epoch 28/50\n",
      "197/197 [==============================] - 6s 29ms/step - loss: 0.0016 - val_loss: 0.0041\n",
      "Epoch 29/50\n",
      "197/197 [==============================] - 6s 29ms/step - loss: 0.0015 - val_loss: 0.0043\n",
      "Epoch 30/50\n",
      "197/197 [==============================] - 6s 28ms/step - loss: 0.0016 - val_loss: 0.0034\n",
      "Epoch 31/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0014 - val_loss: 0.0038\n",
      "Epoch 32/50\n",
      "197/197 [==============================] - 6s 28ms/step - loss: 0.0014 - val_loss: 0.0042\n",
      "Epoch 33/50\n",
      "197/197 [==============================] - 6s 30ms/step - loss: 0.0014 - val_loss: 0.0036\n",
      "Epoch 34/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0014 - val_loss: 0.0042\n",
      "Epoch 35/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0015 - val_loss: 0.0038\n",
      "Epoch 36/50\n",
      "197/197 [==============================] - 6s 28ms/step - loss: 0.0014 - val_loss: 0.0049\n",
      "Epoch 37/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0014 - val_loss: 0.0051\n",
      "Epoch 38/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0015 - val_loss: 0.0045\n",
      "Epoch 39/50\n",
      "197/197 [==============================] - 6s 30ms/step - loss: 0.0015 - val_loss: 0.0054\n",
      "Epoch 40/50\n",
      "197/197 [==============================] - 6s 30ms/step - loss: 0.0015 - val_loss: 0.0041\n",
      "Epoch 41/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0015 - val_loss: 0.0055\n",
      "Epoch 42/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0016 - val_loss: 0.0038\n",
      "Epoch 43/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0015 - val_loss: 0.0050\n",
      "Epoch 44/50\n",
      "197/197 [==============================] - 6s 29ms/step - loss: 0.0014 - val_loss: 0.0040\n",
      "Epoch 45/50\n",
      "197/197 [==============================] - 6s 28ms/step - loss: 0.0015 - val_loss: 0.0042\n",
      "Epoch 46/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0014 - val_loss: 0.0038\n",
      "Epoch 47/50\n",
      "197/197 [==============================] - 5s 27ms/step - loss: 0.0014 - val_loss: 0.0049\n",
      "Epoch 48/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0014 - val_loss: 0.0041\n",
      "Epoch 49/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0014 - val_loss: 0.0037\n",
      "Epoch 50/50\n",
      "197/197 [==============================] - 5s 28ms/step - loss: 0.0014 - val_loss: 0.0044\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1a768931040>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the model on the whole dataset\n",
    "model.fit(data, y_transformed, validation_split=0.35, epochs=50, batch_size=8, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Data Prepration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76/76 [==============================] - 1s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_2018 = model.predict(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_2018 =  pd.DataFrame(pred_2018, columns = ['2018'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_with_2018 = pd.concat([data, y_transformed, pred_2018], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 1/76 [..............................] - ETA: 3s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76/76 [==============================] - 1s 18ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_2019 = model.predict(data_with_2018) \n",
    "pred_2019 =  pd.DataFrame(pred_2019, columns = ['2019'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomass_availability_forecasted_data = pd.concat([location, data_with_2018, pred_2019], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.33144</td>\n",
       "      <td>0.212466</td>\n",
       "      <td>0.225726</td>\n",
       "      <td>0.219358</td>\n",
       "      <td>0.169677</td>\n",
       "      <td>0.231301</td>\n",
       "      <td>0.205046</td>\n",
       "      <td>0.243113</td>\n",
       "      <td>0.180307</td>\n",
       "      <td>0.199934</td>\n",
       "      <td>0.502740</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.41106</td>\n",
       "      <td>0.300711</td>\n",
       "      <td>0.333306</td>\n",
       "      <td>0.309579</td>\n",
       "      <td>0.259861</td>\n",
       "      <td>0.340509</td>\n",
       "      <td>0.328613</td>\n",
       "      <td>0.428428</td>\n",
       "      <td>0.362593</td>\n",
       "      <td>0.334189</td>\n",
       "      <td>0.518164</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.49069</td>\n",
       "      <td>0.370192</td>\n",
       "      <td>0.440955</td>\n",
       "      <td>0.402816</td>\n",
       "      <td>0.350319</td>\n",
       "      <td>0.433291</td>\n",
       "      <td>0.380620</td>\n",
       "      <td>0.486443</td>\n",
       "      <td>0.435923</td>\n",
       "      <td>0.397611</td>\n",
       "      <td>0.511109</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.57031</td>\n",
       "      <td>0.407901</td>\n",
       "      <td>0.471496</td>\n",
       "      <td>0.449079</td>\n",
       "      <td>0.371401</td>\n",
       "      <td>0.475138</td>\n",
       "      <td>0.449872</td>\n",
       "      <td>0.525324</td>\n",
       "      <td>0.485404</td>\n",
       "      <td>0.437328</td>\n",
       "      <td>0.541641</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.64994</td>\n",
       "      <td>0.255002</td>\n",
       "      <td>0.292657</td>\n",
       "      <td>0.292995</td>\n",
       "      <td>0.243953</td>\n",
       "      <td>0.281919</td>\n",
       "      <td>0.291840</td>\n",
       "      <td>0.317984</td>\n",
       "      <td>0.309058</td>\n",
       "      <td>0.257460</td>\n",
       "      <td>0.509233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>72.84432</td>\n",
       "      <td>0.180534</td>\n",
       "      <td>0.180260</td>\n",
       "      <td>0.170496</td>\n",
       "      <td>0.129193</td>\n",
       "      <td>0.180347</td>\n",
       "      <td>0.190154</td>\n",
       "      <td>0.231986</td>\n",
       "      <td>0.181932</td>\n",
       "      <td>0.177813</td>\n",
       "      <td>0.534950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>72.92394</td>\n",
       "      <td>0.051700</td>\n",
       "      <td>0.054713</td>\n",
       "      <td>0.048782</td>\n",
       "      <td>0.044817</td>\n",
       "      <td>0.051860</td>\n",
       "      <td>0.054145</td>\n",
       "      <td>0.065674</td>\n",
       "      <td>0.051462</td>\n",
       "      <td>0.048789</td>\n",
       "      <td>0.564789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>73.00357</td>\n",
       "      <td>0.035414</td>\n",
       "      <td>0.037422</td>\n",
       "      <td>0.033379</td>\n",
       "      <td>0.030625</td>\n",
       "      <td>0.035525</td>\n",
       "      <td>0.037108</td>\n",
       "      <td>0.044995</td>\n",
       "      <td>0.035245</td>\n",
       "      <td>0.034098</td>\n",
       "      <td>0.564997</td>\n",
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       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>73.08319</td>\n",
       "      <td>0.145447</td>\n",
       "      <td>0.121111</td>\n",
       "      <td>0.097915</td>\n",
       "      <td>0.099246</td>\n",
       "      <td>0.114721</td>\n",
       "      <td>0.119751</td>\n",
       "      <td>0.145269</td>\n",
       "      <td>0.113851</td>\n",
       "      <td>0.120770</td>\n",
       "      <td>0.528472</td>\n",
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       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>73.16282</td>\n",
       "      <td>0.088909</td>\n",
       "      <td>0.067580</td>\n",
       "      <td>0.050551</td>\n",
       "      <td>0.055369</td>\n",
       "      <td>0.064035</td>\n",
       "      <td>0.066849</td>\n",
       "      <td>0.081089</td>\n",
       "      <td>0.063546</td>\n",
       "      <td>0.068042</td>\n",
       "      <td>0.549662</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2418 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "      Latitude  Longitude      2010      2011      2012      2013      2014  \\\n",
       "0     24.66818   71.33144  0.212466  0.225726  0.219358  0.169677  0.231301   \n",
       "1     24.66818   71.41106  0.300711  0.333306  0.309579  0.259861  0.340509   \n",
       "2     24.66818   71.49069  0.370192  0.440955  0.402816  0.350319  0.433291   \n",
       "3     24.66818   71.57031  0.407901  0.471496  0.449079  0.371401  0.475138   \n",
       "4     24.66818   71.64994  0.255002  0.292657  0.292995  0.243953  0.281919   \n",
       "...        ...        ...       ...       ...       ...       ...       ...   \n",
       "2413  20.15456   72.84432  0.180534  0.180260  0.170496  0.129193  0.180347   \n",
       "2414  20.15456   72.92394  0.051700  0.054713  0.048782  0.044817  0.051860   \n",
       "2415  20.15456   73.00357  0.035414  0.037422  0.033379  0.030625  0.035525   \n",
       "2416  20.15456   73.08319  0.145447  0.121111  0.097915  0.099246  0.114721   \n",
       "2417  20.15456   73.16282  0.088909  0.067580  0.050551  0.055369  0.064035   \n",
       "\n",
       "          2015      2016      2017      2018      2019  \n",
       "0     0.205046  0.243113  0.180307  0.199934  0.502740  \n",
       "1     0.328613  0.428428  0.362593  0.334189  0.518164  \n",
       "2     0.380620  0.486443  0.435923  0.397611  0.511109  \n",
       "3     0.449872  0.525324  0.485404  0.437328  0.541641  \n",
       "4     0.291840  0.317984  0.309058  0.257460  0.509233  \n",
       "...        ...       ...       ...       ...       ...  \n",
       "2413  0.190154  0.231986  0.181932  0.177813  0.534950  \n",
       "2414  0.054145  0.065674  0.051462  0.048789  0.564789  \n",
       "2415  0.037108  0.044995  0.035245  0.034098  0.564997  \n",
       "2416  0.119751  0.145269  0.113851  0.120770  0.528472  \n",
       "2417  0.066849  0.081089  0.063546  0.068042  0.549662  \n",
       "\n",
       "[2418 rows x 12 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_availability_forecasted_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Latitude     float64\n",
       "Longitude    float64\n",
       "2010         float64\n",
       "2011         float64\n",
       "2012         float64\n",
       "2013         float64\n",
       "2014         float64\n",
       "2015         float64\n",
       "2016         float64\n",
       "2017         float64\n",
       "2018         float32\n",
       "2019         float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_availability_forecasted_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19993404"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_availability_forecasted_data.loc[0,'2018']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count function for positive and negative trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check(data):\n",
    "    positive=0\n",
    "    neg=0\n",
    "    for i,_ in data.iterrows():\n",
    "        if(data.loc[i,'2018']>data.loc[i,'2017'] and data.loc[i,'2019']>data.loc[i,'2018']):\n",
    "            positive+=1\n",
    "        else:\n",
    "            neg+=1\n",
    "    print('Positive tend count : '+str(positive))\n",
    "    print('Negative trend count : '+str(neg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive tend count : 758\n",
      "Negative trend count : 1660\n"
     ]
    }
   ],
   "source": [
    "check(biomass_availability_forecasted_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting forecasted data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "biomass_availability_forecasted_data.to_csv('forecasted.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>2010</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
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       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
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       "      <th>0</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.33144</td>\n",
       "      <td>0.212466</td>\n",
       "      <td>0.225726</td>\n",
       "      <td>0.219358</td>\n",
       "      <td>0.169677</td>\n",
       "      <td>0.231301</td>\n",
       "      <td>0.205046</td>\n",
       "      <td>0.243113</td>\n",
       "      <td>0.180307</td>\n",
       "      <td>0.199934</td>\n",
       "      <td>0.502740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.41106</td>\n",
       "      <td>0.300711</td>\n",
       "      <td>0.333306</td>\n",
       "      <td>0.309579</td>\n",
       "      <td>0.259861</td>\n",
       "      <td>0.340509</td>\n",
       "      <td>0.328613</td>\n",
       "      <td>0.428428</td>\n",
       "      <td>0.362593</td>\n",
       "      <td>0.334189</td>\n",
       "      <td>0.518164</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.49069</td>\n",
       "      <td>0.370192</td>\n",
       "      <td>0.440955</td>\n",
       "      <td>0.402816</td>\n",
       "      <td>0.350319</td>\n",
       "      <td>0.433291</td>\n",
       "      <td>0.380620</td>\n",
       "      <td>0.486443</td>\n",
       "      <td>0.435923</td>\n",
       "      <td>0.397611</td>\n",
       "      <td>0.511109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.57031</td>\n",
       "      <td>0.407901</td>\n",
       "      <td>0.471496</td>\n",
       "      <td>0.449079</td>\n",
       "      <td>0.371401</td>\n",
       "      <td>0.475138</td>\n",
       "      <td>0.449872</td>\n",
       "      <td>0.525324</td>\n",
       "      <td>0.485404</td>\n",
       "      <td>0.437328</td>\n",
       "      <td>0.541641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.66818</td>\n",
       "      <td>71.64994</td>\n",
       "      <td>0.255002</td>\n",
       "      <td>0.292657</td>\n",
       "      <td>0.292995</td>\n",
       "      <td>0.243953</td>\n",
       "      <td>0.281919</td>\n",
       "      <td>0.291840</td>\n",
       "      <td>0.317984</td>\n",
       "      <td>0.309058</td>\n",
       "      <td>0.257460</td>\n",
       "      <td>0.509233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>72.84432</td>\n",
       "      <td>0.180534</td>\n",
       "      <td>0.180260</td>\n",
       "      <td>0.170496</td>\n",
       "      <td>0.129193</td>\n",
       "      <td>0.180347</td>\n",
       "      <td>0.190154</td>\n",
       "      <td>0.231986</td>\n",
       "      <td>0.181932</td>\n",
       "      <td>0.177813</td>\n",
       "      <td>0.534950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>72.92394</td>\n",
       "      <td>0.051700</td>\n",
       "      <td>0.054713</td>\n",
       "      <td>0.048782</td>\n",
       "      <td>0.044817</td>\n",
       "      <td>0.051860</td>\n",
       "      <td>0.054145</td>\n",
       "      <td>0.065674</td>\n",
       "      <td>0.051462</td>\n",
       "      <td>0.048789</td>\n",
       "      <td>0.564789</td>\n",
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       "    <tr>\n",
       "      <th>2415</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>73.00357</td>\n",
       "      <td>0.035414</td>\n",
       "      <td>0.037422</td>\n",
       "      <td>0.033379</td>\n",
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       "      <td>0.035525</td>\n",
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       "      <td>0.035245</td>\n",
       "      <td>0.034098</td>\n",
       "      <td>0.564997</td>\n",
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       "    <tr>\n",
       "      <th>2416</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>73.08319</td>\n",
       "      <td>0.145447</td>\n",
       "      <td>0.121111</td>\n",
       "      <td>0.097915</td>\n",
       "      <td>0.099246</td>\n",
       "      <td>0.114721</td>\n",
       "      <td>0.119751</td>\n",
       "      <td>0.145269</td>\n",
       "      <td>0.113851</td>\n",
       "      <td>0.120770</td>\n",
       "      <td>0.528472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2417</th>\n",
       "      <td>20.15456</td>\n",
       "      <td>73.16282</td>\n",
       "      <td>0.088909</td>\n",
       "      <td>0.067580</td>\n",
       "      <td>0.050551</td>\n",
       "      <td>0.055369</td>\n",
       "      <td>0.064035</td>\n",
       "      <td>0.066849</td>\n",
       "      <td>0.081089</td>\n",
       "      <td>0.063546</td>\n",
       "      <td>0.068042</td>\n",
       "      <td>0.549662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2418 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Latitude  Longitude      2010      2011      2012      2013      2014  \\\n",
       "0     24.66818   71.33144  0.212466  0.225726  0.219358  0.169677  0.231301   \n",
       "1     24.66818   71.41106  0.300711  0.333306  0.309579  0.259861  0.340509   \n",
       "2     24.66818   71.49069  0.370192  0.440955  0.402816  0.350319  0.433291   \n",
       "3     24.66818   71.57031  0.407901  0.471496  0.449079  0.371401  0.475138   \n",
       "4     24.66818   71.64994  0.255002  0.292657  0.292995  0.243953  0.281919   \n",
       "...        ...        ...       ...       ...       ...       ...       ...   \n",
       "2413  20.15456   72.84432  0.180534  0.180260  0.170496  0.129193  0.180347   \n",
       "2414  20.15456   72.92394  0.051700  0.054713  0.048782  0.044817  0.051860   \n",
       "2415  20.15456   73.00357  0.035414  0.037422  0.033379  0.030625  0.035525   \n",
       "2416  20.15456   73.08319  0.145447  0.121111  0.097915  0.099246  0.114721   \n",
       "2417  20.15456   73.16282  0.088909  0.067580  0.050551  0.055369  0.064035   \n",
       "\n",
       "          2015      2016      2017      2018      2019  \n",
       "0     0.205046  0.243113  0.180307  0.199934  0.502740  \n",
       "1     0.328613  0.428428  0.362593  0.334189  0.518164  \n",
       "2     0.380620  0.486443  0.435923  0.397611  0.511109  \n",
       "3     0.449872  0.525324  0.485404  0.437328  0.541641  \n",
       "4     0.291840  0.317984  0.309058  0.257460  0.509233  \n",
       "...        ...       ...       ...       ...       ...  \n",
       "2413  0.190154  0.231986  0.181932  0.177813  0.534950  \n",
       "2414  0.054145  0.065674  0.051462  0.048789  0.564789  \n",
       "2415  0.037108  0.044995  0.035245  0.034098  0.564997  \n",
       "2416  0.119751  0.145269  0.113851  0.120770  0.528472  \n",
       "2417  0.066849  0.081089  0.063546  0.068042  0.549662  \n",
       "\n",
       "[2418 rows x 12 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biomass_availability_forecasted_data"
   ]
  }
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