Rice_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d6e9358e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7e2b6322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To read data set\n",
    "\n",
    "data=pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\crop_production.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6903ef68",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>INDICATOR</th>\n",
       "      <th>SUBJECT</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>FREQUENCY</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AUS</td>\n",
       "      <td>CROPYIELD</td>\n",
       "      <td>RICE</td>\n",
       "      <td>TONNE_HA</td>\n",
       "      <td>A</td>\n",
       "      <td>1990</td>\n",
       "      <td>8.314607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AUS</td>\n",
       "      <td>CROPYIELD</td>\n",
       "      <td>RICE</td>\n",
       "      <td>TONNE_HA</td>\n",
       "      <td>A</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AUS</td>\n",
       "      <td>CROPYIELD</td>\n",
       "      <td>RICE</td>\n",
       "      <td>TONNE_HA</td>\n",
       "      <td>A</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.094340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AUS</td>\n",
       "      <td>CROPYIELD</td>\n",
       "      <td>RICE</td>\n",
       "      <td>TONNE_HA</td>\n",
       "      <td>A</td>\n",
       "      <td>1993</td>\n",
       "      <td>8.336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AUS</td>\n",
       "      <td>CROPYIELD</td>\n",
       "      <td>RICE</td>\n",
       "      <td>TONNE_HA</td>\n",
       "      <td>A</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.537815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index LOCATION  INDICATOR SUBJECT   MEASURE FREQUENCY  TIME     VALUE\n",
       "0      0      AUS  CROPYIELD    RICE  TONNE_HA         A  1990  8.314607\n",
       "1      1      AUS  CROPYIELD    RICE  TONNE_HA         A  1991  8.394737\n",
       "2      2      AUS  CROPYIELD    RICE  TONNE_HA         A  1992  8.094340\n",
       "3      3      AUS  CROPYIELD    RICE  TONNE_HA         A  1993  8.336000\n",
       "4      4      AUS  CROPYIELD    RICE  TONNE_HA         A  1994  8.537815"
      ]
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e5f55bf3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1990</td>\n",
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       "      <th>1</th>\n",
       "      <td>AUS</td>\n",
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       "      <td>1991</td>\n",
       "      <td>8.394737</td>\n",
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       "      <th>2</th>\n",
       "      <td>AUS</td>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.537815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "  LOCATION SUBJECT  TIME     VALUE\n",
       "0      AUS    RICE  1990  8.314607\n",
       "1      AUS    RICE  1991  8.394737\n",
       "2      AUS    RICE  1992  8.094340\n",
       "3      AUS    RICE  1993  8.336000\n",
       "4      AUS    RICE  1994  8.537815"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To select required headings\n",
    "\n",
    "data = data[['LOCATION','SUBJECT','TIME','VALUE']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "17883b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>crop</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
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       "      <td>AUS</td>\n",
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       "      <td>1990</td>\n",
       "      <td>8.314607</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.094340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1993</td>\n",
       "      <td>8.336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.537815</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  area  crop  year     value\n",
       "0  AUS  RICE  1990  8.314607\n",
       "1  AUS  RICE  1991  8.394737\n",
       "2  AUS  RICE  1992  8.094340\n",
       "3  AUS  RICE  1993  8.336000\n",
       "4  AUS  RICE  1994  8.537815"
      ]
     },
     "execution_count": 59,
     "metadata": {},
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    }
   ],
   "source": [
    "# To rename column heads\n",
    "\n",
    "data=data.rename(columns ={'LOCATION':'area','SUBJECT':'crop','TIME':'year','VALUE':'value'})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fe3567a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To select one crop 'RICE'\n",
    "\n",
    "data = data[data['crop'] == 'RICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9238fca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5148 entries, 0 to 16945\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   area    5148 non-null   object \n",
      " 1   crop    5148 non-null   object \n",
      " 2   year    5148 non-null   int64  \n",
      " 3   value   5076 non-null   float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 201.1+ KB\n"
     ]
    },
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       "      <th>crop</th>\n",
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       "      <td>AUS</td>\n",
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       "      <td>AUS</td>\n",
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       "      <td>1992</td>\n",
       "      <td>8.094340</td>\n",
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       "      <th>3</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1993</td>\n",
       "      <td>8.336000</td>\n",
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       "      <th>4</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.537815</td>\n",
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       "      <th>16941</th>\n",
       "      <td>HTI</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2021</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16942</th>\n",
       "      <td>HTI</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2022</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16943</th>\n",
       "      <td>HTI</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2023</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16944</th>\n",
       "      <td>HTI</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2024</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16945</th>\n",
       "      <td>HTI</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2025</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5148 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      area  crop  year     value\n",
       "0      AUS  RICE  1990  8.314607\n",
       "1      AUS  RICE  1991  8.394737\n",
       "2      AUS  RICE  1992  8.094340\n",
       "3      AUS  RICE  1993  8.336000\n",
       "4      AUS  RICE  1994  8.537815\n",
       "...    ...   ...   ...       ...\n",
       "16941  HTI  RICE  2021       NaN\n",
       "16942  HTI  RICE  2022       NaN\n",
       "16943  HTI  RICE  2023       NaN\n",
       "16944  HTI  RICE  2024       NaN\n",
       "16945  HTI  RICE  2025       NaN\n",
       "\n",
       "[5148 rows x 4 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4ed6d86c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>crop</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
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       "      <td>8.314607</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1991</td>\n",
       "      <td>8.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1992</td>\n",
       "      <td>8.094340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1993</td>\n",
       "      <td>8.336000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AUS</td>\n",
       "      <td>RICE</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.537815</td>\n",
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       "    <tr>\n",
       "      <th>10099</th>\n",
       "      <td>TUR</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.120146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10100</th>\n",
       "      <td>TUR</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.120240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10101</th>\n",
       "      <td>TUR</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2020</td>\n",
       "      <td>0.120109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10102</th>\n",
       "      <td>TUR</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2021</td>\n",
       "      <td>0.119900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10103</th>\n",
       "      <td>TUR</td>\n",
       "      <td>RICE</td>\n",
       "      <td>2022</td>\n",
       "      <td>0.119647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4375 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      area  crop  year     value\n",
       "0      AUS  RICE  1990  8.314607\n",
       "1      AUS  RICE  1991  8.394737\n",
       "2      AUS  RICE  1992  8.094340\n",
       "3      AUS  RICE  1993  8.336000\n",
       "4      AUS  RICE  1994  8.537815\n",
       "...    ...   ...   ...       ...\n",
       "10099  TUR  RICE  2018  0.120146\n",
       "10100  TUR  RICE  2019  0.120240\n",
       "10101  TUR  RICE  2020  0.120109\n",
       "10102  TUR  RICE  2021  0.119900\n",
       "10103  TUR  RICE  2022  0.119647\n",
       "\n",
       "[4375 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove rows with zero values\n",
    "\n",
    "# replace 0 values with NaN values\n",
    "data = data.replace(0, float('nan'))\n",
    "\n",
    "# drop rows with NaN values\n",
    "data = data.dropna()\n",
    "\n",
    "\n",
    "# Filter the data to only include rows where the year is before 2023\n",
    "data = data[data['year'] < 2023]\n",
    "\n",
    "\n",
    "data.to_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\single_crop_1.csv', index=False)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "20fdc62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To remove Crop column\n",
    "\n",
    "data=pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\single_crop_1.csv')\n",
    "\n",
    "data = data.drop('crop', axis=1)\n",
    "\n",
    "data\n",
    "\n",
    "data.to_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\single_crop_2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "188d236a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the data by country and year and calculate the mean rice yield for each group\n",
    "\n",
    "data = data.groupby(['area', 'year'])['value'].mean().reset_index()\n",
    "data\n",
    "# Now \"country_means\" is a new DataFrame with columns \"area\", \"year\", and \"value\" (which is the mean value for each area and year)\n",
    "\n",
    "\n",
    "data.to_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\final_2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3c070632",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\final_2.csv')\n",
    "\n",
    "data = data.loc[data.area=='ARG', :]\n",
    "data\n",
    "\n",
    "data.to_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8e7f4d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n",
    "\n",
    "import matplotlib.pylab as plt #for visualization\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "from matplotlib.pylab import rcParams\n",
    "from matplotlib import colors\n",
    "\n",
    "#for making sure matplotlib plots are generated in codlab notebook itself\n",
    "%matplotlib inline      \n",
    "       \n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from scipy.stats import boxcox\n",
    "\n",
    "rcParams['figure.figsize'] = 10, 6\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "eacec7e3",
   "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>area</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARG</td>\n",
       "      <td>1990</td>\n",
       "      <td>1.283776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARG</td>\n",
       "      <td>1991</td>\n",
       "      <td>1.195437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARG</td>\n",
       "      <td>1992</td>\n",
       "      <td>1.121050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARG</td>\n",
       "      <td>1993</td>\n",
       "      <td>1.110340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>1994</td>\n",
       "      <td>1.351937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  area  year     value\n",
       "0  ARG  1990  1.283776\n",
       "1  ARG  1991  1.195437\n",
       "2  ARG  1992  1.121050\n",
       "3  ARG  1993  1.110340\n",
       "4  ARG  1994  1.351937"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_3.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a0e5b1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_3.csv')\n",
    "\n",
    "data = data.drop('area', axis=1)\n",
    "\n",
    "data\n",
    "\n",
    "data.to_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ceab9c76",
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1.283776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>1.195437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>1.121050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>1.110340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>1.351937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Value\n",
       "Year          \n",
       "1990  1.283776\n",
       "1991  1.195437\n",
       "1992  1.121050\n",
       "1993  1.110340\n",
       "1994  1.351937"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename dataset columns and Parse strings to datetime type and \n",
    "\n",
    "data=pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "data = data.rename(columns={\"year\": \"Year\", \"value\": \"Value\"})\n",
    "\n",
    "indexed_data = data.set_index(['Year'])\n",
    "indexed_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cfb0e8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Rice Production')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Stationarity\n",
    "\n",
    "## Plot time and frequency graphs\n",
    "\n",
    "plt.plot(indexed_data, color = 'red')\n",
    "plt.xlabel('Year', size = 12)\n",
    "plt.ylabel('Rice Production', size = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "477205be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the frequency graph\n",
    "\n",
    "plt.hist(indexed_data['Value'], color = 'blue')\n",
    "plt.xlabel('Rice Production', size = 12)\n",
    "plt.ylabel('Frequency', size = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a66f49",
   "metadata": {},
   "source": [
    "# Question 1\n",
    "\n",
    "Considering the time graph above, which feature of time series can you observe in the dataset?\n",
    "\n",
    "# Answer\n",
    "\n",
    "Determining and plotting rolling statistics\n",
    "\n",
    "As our project has been calculated for a year, the mean and SD of each month is not calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "96ca2bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Rolling Statistics')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting rolling statistics\n",
    "\n",
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "# set the year column as the index\n",
    "data.set_index('year', inplace=True)\n",
    "\n",
    "# calculate rolling mean for 3 years\n",
    "rolling_mean = data.rolling(window=3).mean()\n",
    "\n",
    "# calculate rolling SD for 3 years\n",
    "rolling_std = data.rolling(window=3).std()\n",
    "\n",
    "#plot rolling statistics\n",
    "plt.plot(indexed_data, color='cornflowerblue', label='Original')\n",
    "plt.plot(rolling_mean, color='firebrick', label='Rolling Mean')\n",
    "plt.plot(rolling_std, color='limegreen', label='Rolling Std')\n",
    "plt.xlabel('year', size = 12)\n",
    "plt.ylabel('Rice Production', size  = 12)\n",
    "plt.legend(loc='best')\n",
    "plt.title('Rolling Statistics', size = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8890250f",
   "metadata": {},
   "source": [
    "# Non-stationary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92341d5f",
   "metadata": {},
   "source": [
    "Why make a non-stationary series stationary before forecasting?\n",
    "Forecasting a stationary series is relatively easy and the forecasts are more reliable.\n",
    "\n",
    "An important reason is, autoregressive forecasting models are essentially linear regression models that utilize the lag(s) of the series itself as predictors.\n",
    "\n",
    "We know that linear regression works best if the predictors (X variables) are not correlated against each other. So, stationarizing the series solves this problem since it removes any persistent autocorrelation, thereby making the predictors(lags of the series) in the forecasting models nearly independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "92a882aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistic                 -1.505032\n",
      "p-value                         0.531021\n",
      "#Lags Used                      1.000000\n",
      "Number of Observations Used    31.000000\n",
      "Critical Value (1%)            -3.661429\n",
      "Critical Value (5%)            -2.960525\n",
      "Critical Value (10%)           -2.619319\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "data_test = adfuller(indexed_data['Value'], autolag='AIC')\n",
    "\n",
    "data_output = pd.Series(data_test[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "for key,value in data_test[4].items():\n",
    "    data_output['Critical Value (%s)'%key] = value\n",
    "    \n",
    "print(data_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ead3391",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "\n",
    "In one sentence, interpret the result of the AD-Fuller Test\n",
    "\n",
    "# Answer\n",
    "\n",
    "Coupling the AD-Fuller check together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ef3f5392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic: -1.5050322246670886\n",
      "p-value: 0.5310205167491356\n",
      "Critical value: -2.960525341210433\n",
      "The time series is non-stationary\n"
     ]
    }
   ],
   "source": [
    "# For single country\n",
    "\n",
    "# To check stationary or not\n",
    "# Augmented Dickey Fuller test (ADF Test) is a common statistical test used to test\n",
    "# whether a given Time series is stationary or not\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "# Load the time series data from a CSV file\n",
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "# Perform the ADF test\n",
    "result = adfuller(data['value'])\n",
    "\n",
    "# Extract and print the test statistics and p-value\n",
    "ADF_test = result[0]   #result[0] returns the test statistic value\n",
    "p_value = result[1]    #result[1] corresponds to the p-value in the ADF test\n",
    "crit_value = result[4]['5%'] #result[4] corresponds to the critical value [retrieve the critical value for a given level of confidence (e.g., 5%)]\n",
    "print('ADF Test Statistic:', ADF_test)\n",
    "print('p-value:', p_value)\n",
    "print('Critical value:', crit_value)\n",
    "\n",
    "# Interpret the test results\n",
    "if p_value < 0.05 and ADF_test < crit_value :\n",
    "    print('The time series is stationary')\n",
    "else:\n",
    "    print('The time series is non-stationary')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d6fceddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location ARG:\n",
      "ADF Statistic: -1.5050322246670886\n",
      "p-value: 0.5310205167491356\n",
      "Critical values: {'1%': -3.661428725118324, '5%': -2.960525341210433, '10%': -2.6193188033298647}\n",
      "The time series is non-stationary\n",
      "Location AUS:\n",
      "ADF Statistic: -4.894773042043111\n",
      "p-value: 3.570063580917523e-05\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is stationary\n",
      "Location BGD:\n",
      "ADF Statistic: -1.1189845597087311\n",
      "p-value: 0.7074420116508375\n",
      "Critical values: {'1%': -3.769732625845229, '5%': -3.005425537190083, '10%': -2.6425009917355373}\n",
      "The time series is non-stationary\n",
      "Location BRA:\n",
      "ADF Statistic: -1.480771943780561\n",
      "p-value: 0.5430008368118021\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location BRICS:\n",
      "ADF Statistic: -2.9980174824897903\n",
      "p-value: 0.03507995770964904\n",
      "Critical values: {'1%': -3.7529275211638033, '5%': -2.998499866852963, '10%': -2.6389669754253307}\n",
      "The time series is non-stationary\n",
      "Location CAN:\n",
      "ADF Statistic: -31.739621100278196\n",
      "p-value: 0.0\n",
      "Critical values: {'1%': -3.769732625845229, '5%': -3.005425537190083, '10%': -2.6425009917355373}\n",
      "The time series is stationary\n",
      "Location CHE:\n",
      "ADF Statistic: -1.3899392452293182\n",
      "p-value: 0.5870897540011547\n",
      "Critical values: {'1%': -3.6699197407407405, '5%': -2.9640707407407407, '10%': -2.621171111111111}\n",
      "The time series is non-stationary\n",
      "Location CHL:\n",
      "ADF Statistic: -1.969130552862282\n",
      "p-value: 0.30027009246790737\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location CHN:\n",
      "ADF Statistic: -2.11031018180471\n",
      "p-value: 0.24038908665843867\n",
      "Critical values: {'1%': -3.769732625845229, '5%': -3.005425537190083, '10%': -2.6425009917355373}\n",
      "The time series is non-stationary\n",
      "Location COL:\n",
      "ADF Statistic: -1.742699556164847\n",
      "p-value: 0.40925742057501147\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location DZA:\n",
      "ADF Statistic: -2.6023414310753017\n",
      "p-value: 0.09251430624659174\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location EGY:\n",
      "ADF Statistic: -2.263608442170408\n",
      "p-value: 0.18397364148119527\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location ETH:\n",
      "ADF Statistic: -1.6773220169962795\n",
      "p-value: 0.44283309605790094\n",
      "Critical values: {'1%': -3.7112123008648155, '5%': -2.981246804733728, '10%': -2.6300945562130176}\n",
      "The time series is non-stationary\n",
      "Location EU28:\n",
      "ADF Statistic: -4.913125918066843\n",
      "p-value: 3.2879735363789286e-05\n",
      "Critical values: {'1%': -3.6790595944893187, '5%': -2.9678817237279103, '10%': -2.6231583472057074}\n",
      "The time series is stationary\n",
      "Location GHA:\n",
      "ADF Statistic: 0.281089807013298\n",
      "p-value: 0.9764600684225204\n",
      "Critical values: {'1%': -3.6699197407407405, '5%': -2.9640707407407407, '10%': -2.621171111111111}\n",
      "The time series is non-stationary\n",
      "Location HTI:\n",
      "ADF Statistic: -2.6401721041423745\n",
      "p-value: 0.08497544315133937\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location IDN:\n",
      "ADF Statistic: 0.5430256048204388\n",
      "p-value: 0.9861237914019203\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location IND:\n",
      "ADF Statistic: -3.507257139233516\n",
      "p-value: 0.007802659724813724\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is stationary\n",
      "Location IRN:\n",
      "ADF Statistic: -1.2100510841694974\n",
      "p-value: 0.6692571131088448\n",
      "Critical values: {'1%': -3.6889256286443146, '5%': -2.9719894897959187, '10%': -2.6252957653061224}\n",
      "The time series is non-stationary\n",
      "Location ISR:\n",
      "ADF Statistic: -1.989752524005506\n",
      "p-value: 0.2910842568812274\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location JPN:\n",
      "ADF Statistic: -2.1243129417353575\n",
      "p-value: 0.234851281755927\n",
      "Critical values: {'1%': -3.7529275211638033, '5%': -2.998499866852963, '10%': -2.6389669754253307}\n",
      "The time series is non-stationary\n",
      "Location KAZ:\n",
      "ADF Statistic: 0.4842436088470499\n",
      "p-value: 0.9843862431891315\n",
      "Critical values: {'1%': -3.7112123008648155, '5%': -2.981246804733728, '10%': -2.6300945562130176}\n",
      "The time series is non-stationary\n",
      "Location KOR:\n",
      "ADF Statistic: -0.3118886029393552\n",
      "p-value: 0.9238035915390196\n",
      "Critical values: {'1%': -3.7112123008648155, '5%': -2.981246804733728, '10%': -2.6300945562130176}\n",
      "The time series is non-stationary\n",
      "Location MEX:\n",
      "ADF Statistic: -2.03802519083286\n",
      "p-value: 0.270153164774374\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location MOZ:\n",
      "ADF Statistic: -1.9290500904052825\n",
      "p-value: 0.31852082821712346\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location MYS:\n",
      "ADF Statistic: -1.728657834414634\n",
      "p-value: 0.4164108512125294\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location NGA:\n",
      "ADF Statistic: -0.4561369729581115\n",
      "p-value: 0.9002956150938254\n",
      "Critical values: {'1%': -3.6699197407407405, '5%': -2.9640707407407407, '10%': -2.621171111111111}\n",
      "The time series is non-stationary\n",
      "Location NOR:\n",
      "ADF Statistic: -2.8535080867402427\n",
      "p-value: 0.051027647333504414\n",
      "Critical values: {'1%': -3.7238633119999998, '5%': -2.98648896, '10%': -2.6328004}\n",
      "The time series is non-stationary\n",
      "Location OECD:\n",
      "ADF Statistic: -1.5990724097944842\n",
      "p-value: 0.48405941542425146\n",
      "Critical values: {'1%': -3.8092091249999998, '5%': -3.0216450000000004, '10%': -2.6507125}\n",
      "The time series is non-stationary\n",
      "Location PAK:\n",
      "ADF Statistic: -1.590332400263461\n",
      "p-value: 0.4884480443878833\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location PER:\n",
      "ADF Statistic: -3.525375721534057\n",
      "p-value: 0.007358812303796297\n",
      "Critical values: {'1%': -3.769732625845229, '5%': -3.005425537190083, '10%': -2.6425009917355373}\n",
      "The time series is stationary\n",
      "Location PHL:\n",
      "ADF Statistic: -1.0388594650778544\n",
      "p-value: 0.738828763817337\n",
      "Critical values: {'1%': -3.6790595944893187, '5%': -2.9678817237279103, '10%': -2.6231583472057074}\n",
      "The time series is non-stationary\n",
      "Location PRY:\n",
      "ADF Statistic: 1.6525923314450555\n",
      "p-value: 0.9980070856074664\n",
      "Critical values: {'1%': -3.7112123008648155, '5%': -2.981246804733728, '10%': -2.6300945562130176}\n",
      "The time series is non-stationary\n",
      "Location RUS:\n",
      "ADF Statistic: -1.5920636864482443\n",
      "p-value: 0.4875789608903758\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location SAU:\n",
      "ADF Statistic: -2.7449518275287206\n",
      "p-value: 0.06657737796206184\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location SDN:\n",
      "ADF Statistic: -0.2467673313619937\n",
      "p-value: 0.9327126110302244\n",
      "Critical values: {'1%': -3.661428725118324, '5%': -2.960525341210433, '10%': -2.6193188033298647}\n",
      "The time series is non-stationary\n",
      "Location SSA:\n",
      "ADF Statistic: -1.6286471572678785\n",
      "p-value: 0.46819892433120497\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location THA:\n",
      "ADF Statistic: -3.0943679685544576\n",
      "p-value: 0.02697280131397712\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is stationary\n",
      "Location TUR:\n",
      "ADF Statistic: -0.9330909014841476\n",
      "p-value: 0.7768799180541925\n",
      "Critical values: {'1%': -3.661428725118324, '5%': -2.960525341210433, '10%': -2.6193188033298647}\n",
      "The time series is non-stationary\n",
      "Location TZA:\n",
      "ADF Statistic: -2.3070406838209205\n",
      "p-value: 0.16970726501086025\n",
      "Critical values: {'1%': -3.6699197407407405, '5%': -2.9640707407407407, '10%': -2.621171111111111}\n",
      "The time series is non-stationary\n",
      "Location UKR:\n",
      "ADF Statistic: -3.305656376872244\n",
      "p-value: 0.01462751981462202\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is stationary\n",
      "Location URY:\n",
      "ADF Statistic: -0.4715089897378519\n",
      "p-value: 0.8974574373633365\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location USA:\n",
      "ADF Statistic: -2.3466700058903243\n",
      "p-value: 0.15735724623449399\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location VNM:\n",
      "ADF Statistic: -1.7310107312869334\n",
      "p-value: 0.4152097772697127\n",
      "Critical values: {'1%': -3.661428725118324, '5%': -2.960525341210433, '10%': -2.6193188033298647}\n",
      "The time series is non-stationary\n",
      "Location WLD:\n",
      "ADF Statistic: -3.469967127786094\n",
      "p-value: 0.008792744933910733\n",
      "Critical values: {'1%': -3.6790595944893187, '5%': -2.9678817237279103, '10%': -2.6231583472057074}\n",
      "The time series is stationary\n",
      "Location ZAF:\n",
      "ADF Statistic: -1.6792491815064001\n",
      "p-value: 0.4418343825792854\n",
      "Critical values: {'1%': -3.653519805908203, '5%': -2.9572185644531253, '10%': -2.6175881640625}\n",
      "The time series is non-stationary\n",
      "Location ZMB:\n",
      "ADF Statistic: -1.4759650807775084\n",
      "p-value: 0.5453658038806803\n",
      "Critical values: {'1%': -3.6790595944893187, '5%': -2.9678817237279103, '10%': -2.6231583472057074}\n",
      "The time series is non-stationary\n"
     ]
    }
   ],
   "source": [
    "# Grouped area and year together\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "# Load the time series data from a CSV file\n",
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\final_2.csv')\n",
    "\n",
    "# Group the data by location and year, and calculate the mean of the values for each group\n",
    "grouped_data = data.groupby(['area', 'year'])['value'].mean()\n",
    "\n",
    "# Loop over each location and run the ADF test on the time series\n",
    "for location in grouped_data.index.levels[0]: #to loop over the unique values in the first level of a multi-level index\n",
    "    location_data = grouped_data.loc[location]\n",
    "    result = adfuller(location_data)\n",
    "    print(f\"Location {location}:\")\n",
    "    print(f\"ADF Statistic: {result[0]}\")   #result[0] returns the test statistic value\n",
    "    print(f\"p-value: {result[1]}\")    #result[1] corresponds to the p-value in the ADF test\n",
    "    print(f\"Critical values: {result[4]}\") #result[4] corresponds to the critical value [retrieve the critical value for a given level of confidence (e.g., 5%)]\n",
    "    if result[1] < 0.05 and result[0] < result[4]['5%']:\n",
    "        print('The time series is stationary')\n",
    "    else:\n",
    "        print('The time series is non-stationary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ce8990e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.51\n",
      "p-value: 0.531\n",
      "#Lags Used: 1\n",
      "#Number of Observations Used: 31\n",
      "##################################\n",
      "Critical Values:\n",
      "1%: -3.66\n",
      "5%: -2.96\n",
      "10%: -2.62\n",
      "##################################\n",
      "ADF > Critical Values\n",
      "Failed to reject null hypothesis, time series is non-stationary.\n"
     ]
    }
   ],
   "source": [
    "# Coupling AD-Fuller check with rolling statistics\n",
    "\n",
    "def adfuller_test(timeseries, transformation_type, window = 3):\n",
    "    movingAverage = timeseries.rolling(window).mean()\n",
    "    movingSTD = timeseries.rolling(window).std()\n",
    "    \n",
    "    orig = plt.plot(timeseries, color='cornflowerblue', label='Original')\n",
    "    mean = plt.plot(movingAverage, color='firebrick', label='Rolling Mean')\n",
    "    std = plt.plot(movingSTD, color='limegreen', label='Rolling Std')\n",
    "    plt.legend(loc = 'best')\n",
    "    plt.title(f'{transformation_type} Rolling Statistics', size = 14)\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    adf = adfuller(timeseries, autolag='AIC') # autolag='AIC': to automatically determine the lag length for the ADF test\n",
    " \n",
    "    print(f'ADF Statistic: {adf[0]:.3}')\n",
    "    print(f'p-value: {adf[1]:.3}')\n",
    "    print(f'#Lags Used: {adf[2]}')\n",
    "    print(f'#Number of Observations Used: {adf[3]}')\n",
    "    print(\"##################################\")  \n",
    "    print('Critical Values:')\n",
    "    \n",
    "    for key, ts in adf[4].items():\n",
    "         print(f'{key}: {ts:.3}')\n",
    "    print(\"##################################\")\n",
    "    \n",
    "    if adf[0] > adf[4][\"5%\"]:\n",
    "        print(\"ADF > Critical Values\")\n",
    "        print (\"Failed to reject null hypothesis, time series is non-stationary.\")\n",
    "    else:\n",
    "        print(\"ADF < Critical Values\")\n",
    "        print (\"Reject null hypothesis, time series is stationary.\")\n",
    "        \n",
    "adfuller_test(indexed_data, 'Non transformation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a0e305",
   "metadata": {},
   "source": [
    "# Data Transformation to Achieve Stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "07fe280e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After Logarithmic Transformation')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logarithmic Transformation\n",
    "\n",
    "indexed_data_log_scaled = np.log(indexed_data)\n",
    "\n",
    "plt.plot(indexed_data_log_scaled, color = 'cornflowerblue')\n",
    "plt.xlabel('Year', size = 12)\n",
    "plt.ylabel('Rice Production', size = 12)\n",
    "plt.title(\"After Logarithmic Transformation\", size = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "63ab2945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After Moving Average')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Removing Trend in Moving Average\n",
    "\n",
    "moving_avg = indexed_data_log_scaled.rolling(window=12).mean()\n",
    "indexed_data_log_scaled_ma = indexed_data_log_scaled - moving_avg\n",
    "\n",
    "indexed_data_log_scaled_ma.dropna(inplace=True) #Remove NAN values\n",
    "\n",
    "plt.plot(indexed_data_log_scaled_ma, color = 'cornflowerblue')\n",
    "plt.xlabel('Year', size = 12)\n",
    "plt.ylabel('Rice Production', size = 12)\n",
    "plt.title(\"After Moving Average\", size = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "26857684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After Exponential Decay Transformation')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exponential Decay Transformation\n",
    "# Exponential weightes moving average\n",
    "# halflife: number of time periods for the weighting to decrease by half\n",
    "\n",
    "indexed_data_log_scaled_ma_ed = indexed_data_log_scaled_ma.ewm(halflife=12, min_periods=0, adjust=True).mean()\n",
    "indexed_data_log_scaled_minus_ma_ed = indexed_data_log_scaled_ma - indexed_data_log_scaled_ma_ed\n",
    "\n",
    "plt.plot(indexed_data_log_scaled_minus_ma_ed, color='cornflowerblue')\n",
    "plt.xlabel('Year', size = 12)\n",
    "plt.ylabel('Rice Production', size = 12)\n",
    "plt.title(\"After Exponential Decay Transformation\", size = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "43e59a63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -7.51\n",
      "p-value: 4e-11\n",
      "#Lags Used: 9\n",
      "#Number of Observations Used: 12\n",
      "##################################\n",
      "Critical Values:\n",
      "1%: -4.14\n",
      "5%: -3.15\n",
      "10%: -2.71\n",
      "##################################\n",
      "ADF < Critical Values\n",
      "Reject null hypothesis, time series is stationary.\n"
     ]
    }
   ],
   "source": [
    "adfuller_test(indexed_data_log_scaled_minus_ma_ed, \"After Exponential Decay Transformation\", window = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cb1583f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'After Time Shift Transformation')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Time Shift Transformation\n",
    "# scaled.shift(): to shift the index by a specified number of periods\n",
    "\n",
    "indexed_data_log_scaled_shift = indexed_data_log_scaled - indexed_data_log_scaled.shift()\n",
    "\n",
    "plt.plot(indexed_data_log_scaled_shift, color='cornflowerblue')\n",
    "plt.xlabel('Year', size = 12)\n",
    "plt.ylabel('Rice Production', size = 12)\n",
    "plt.title(\"After Time Shift Transformation\", size = 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d5a74dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -4.47\n",
      "p-value: 0.000225\n",
      "#Lags Used: 3\n",
      "#Number of Observations Used: 28\n",
      "##################################\n",
      "Critical Values:\n",
      "1%: -3.69\n",
      "5%: -2.97\n",
      "10%: -2.63\n",
      "##################################\n",
      "ADF < Critical Values\n",
      "Reject null hypothesis, time series is stationary.\n"
     ]
    }
   ],
   "source": [
    "indexed_data_log_scaled_shift.dropna(inplace=True) #Remove NAN values\n",
    "adfuller_test(indexed_data_log_scaled_shift, \"After Time Shift Transformation\", window = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0033672",
   "metadata": {},
   "source": [
    "# Question 3\n",
    "Considering the AD-Fuller Test results from the two transformation\n",
    "A: State observations\n",
    "B: Which transformation will yield a better forecast result and why?\n",
    "    \n",
    "# Answer\n",
    "Decompose and plot graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c1dc9306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set the date column as the index and convert to DatetimeIndex\n",
    "\n",
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "data.set_index(['year'], inplace=True)\n",
    "df.drop('area', axis=1, inplace=True)\n",
    "\n",
    "# set the frequency of the DatetimeIndex\n",
    "data.index.freq = 'Y'\n",
    "\n",
    "# apply seasonal decomposition\n",
    "result = seasonal_decompose(data, model='additive', period=1)\n",
    "\n",
    "# plot the decomposed time series\n",
    "result.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2232f177",
   "metadata": {},
   "source": [
    "# Plotting ACF and PACF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "45f84b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "auto_c_f = acf(indexed_data_log_scaled_minus_ma_ed, nlags=10)\n",
    "partial_auto_c_f = pacf(indexed_data_log_scaled_minus_ma_ed, nlags=10, method='ols')\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize =(12,5)) #to plot 2 different graphs\n",
    "\n",
    "# plot ACF\n",
    "plt.subplot(121)\n",
    "plt.plot(auto_c_f)\n",
    "plt.axhline(y=0, linestyle='--', color='green')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(indexed_data_log_scaled_minus_ma_ed)),linestyle='--', color='blue')\n",
    "plt.axhline(y=1.96/np.sqrt(len(indexed_data_log_scaled_minus_ma_ed)),linestyle='--', color='blue')\n",
    "plt.title('Autocorrelation Function', size = 14)  \n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(partial_auto_c_f)\n",
    "plt.axhline(y=0, linestyle='--', color='green')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(indexed_data_log_scaled_minus_ma_ed)), linestyle='--', color='blue')\n",
    "plt.axhline(y=1.96/np.sqrt(len(indexed_data_log_scaled_minus_ma_ed)),linestyle='--', color='blue')\n",
    "plt.title('Partial Autocorrelation Function', size = 14)\n",
    "            \n",
    "plt.tight_layout() #to prevent overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c1d647",
   "metadata": {},
   "source": [
    "# Question 4\n",
    "\n",
    "Determine the value of p (order) and q (moving average) from the graphs PACF and ACF, respectively\n",
    "\n",
    "## Answer\n",
    "\n",
    "Building Forecasting Models\n",
    "\n",
    "Note ARIMA is AR+I+MA. Before, we see an ARIMA model, let us check the results of the individual AR & MA model. Note that, these models will give a value of MSE. Lower MSE values indicate a better model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7977e42",
   "metadata": {},
   "source": [
    "# AR (AutoRegression) Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bc826a7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AR MSE: 0.01021\n"
     ]
    }
   ],
   "source": [
    "ar_values = indexed_data_log_scaled_minus_ma_ed.values\n",
    "\n",
    "#Split the data into two part such that our models can be used \n",
    "#to predict the last 10 days of rice production in the dataset given all prior observations\n",
    "train = ar_values[1:len(ar_values)-10]\n",
    "test = ar_values[len(ar_values)-10:]\n",
    "\n",
    "#Build the AR model\n",
    "model = ARIMA(train, order=(2,1,0))\n",
    "AR_model = model.fit()\n",
    "\n",
    "#Predict the rice production for the next 10 days \n",
    "predictions = AR_model.predict(start=len(train), end=len(train) + len(test)-1, dynamic=False)\n",
    "ar_score = mean_squared_error(test, predictions)\n",
    "print(f'AR MSE: {ar_score:.4}')\n",
    "\n",
    "#When an AR model is fit to a time series, the coefficients of the model are estimated using a technique, MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e8f0ddfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23a635b1a30>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the AR precictions\n",
    "\n",
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "# extract the time series data and predicted values\n",
    "value = data['value']\n",
    "year = data['year']\n",
    "\n",
    "plt.plot(value, label = \"observations\", color = \"cornflowerblue\")\n",
    "plt.plot(year,label = \"forecasts\", color='darkorange')\n",
    "plt.title(\"AR Model\", size = 14)\n",
    "plt.legend(loc = 'upper left')\n",
    "\n",
    "#straight line because the model is a linear regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4da4795",
   "metadata": {},
   "source": [
    "# Moving Average Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1217c8ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MA MSE: 0.005435\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "model = ARIMA(train, order=(0,1,2))\n",
    "start_params = [0.1, 0.1, 0.1] #holds a list of starting values for the parameters of a statistical model\n",
    "MA_model = model.fit(start_params=start_params)\n",
    "\n",
    "predictions = MA_model.predict(start=len(train), end=len(train)+len(test)-1, dynamic=False)\n",
    "ma_score = mean_squared_error(test, predictions)\n",
    "print(f'MA MSE: {ma_score:.4}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37663ce2",
   "metadata": {},
   "source": [
    "# Plot MA forecast "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d31d1389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23a64b89e20>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "# extract the time series data and predicted values\n",
    "value = data['value']\n",
    "year = data['year']\n",
    "\n",
    "#plot the MA precictions\n",
    "plt.plot(value, label = \"observations\", color = \"cornflowerblue\")\n",
    "plt.plot(year,label = \"forecasts\", color='darkorange')\n",
    "plt.title(\"MA Model\", size = 14)\n",
    "plt.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e293d79e",
   "metadata": {},
   "source": [
    "# ARIMA (AutoRegression Integrated Moving Average) Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c1f1ec18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA MSE: 0.006285\n"
     ]
    }
   ],
   "source": [
    "# create a simpler AR model with 2 lags\n",
    "model = ARIMA(train, order=(2, 0, 0))\n",
    "ARIMA_model = model.fit(disp=-1)\n",
    "\n",
    "# generate predictions using the fitted model\n",
    "predictions = ARIMA_model.predict(start=len(train), end=len(train)+len(test)-1, dynamic=False)\n",
    "\n",
    "# compute mean squared error\n",
    "arima_score = mean_squared_error(test, predictions)\n",
    "print(f'ARIMA MSE: {arima_score:.4}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee7edf2",
   "metadata": {},
   "source": [
    "## ARIMA Forecast Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "361b17b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23a649c8670>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "# extract the time series data and predicted values\n",
    "value = data['value']\n",
    "year = data['year']\n",
    "\n",
    "#plot the ARIMA precictions\n",
    "\n",
    "plt.plot(year, label = \"observations\", color = \"cornflowerblue\")\n",
    "plt.plot(value,label = \"forecasts\", color='darkorange')\n",
    "plt.title(\"ARIMA Model\", size = 14)\n",
    "plt.legend(loc = 'upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e581b5d7",
   "metadata": {},
   "source": [
    "# Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df1f710",
   "metadata": {},
   "source": [
    "## Mean Square Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7b1d4028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_7d494_row0_col0{\n",
       "            background-color:  #edf4fc;\n",
       "        }#T_7d494_row1_col0{\n",
       "            background-color:  #dbe9f6;\n",
       "        }#T_7d494_row2_col0{\n",
       "            background-color:  #56a0ce;\n",
       "        }</style><table id=\"T_7d494_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MSE</th>    </tr>    <tr>        <th class=\"index_name level0\" >Model</th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7d494_level0_row0\" class=\"row_heading level0 row0\" >Moving Average</th>\n",
       "                        <td id=\"T_7d494_row0_col0\" class=\"data row0 col0\" >0.005435</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d494_level0_row1\" class=\"row_heading level0 row1\" >ARIMA</th>\n",
       "                        <td id=\"T_7d494_row1_col0\" class=\"data row1 col0\" >0.006285</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7d494_level0_row2\" class=\"row_heading level0 row2\" >Autoregression</th>\n",
       "                        <td id=\"T_7d494_row2_col0\" class=\"data row2 col0\" >0.010213</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23a64f0ef70>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data = pd.read_csv('C:\\\\Users\\\\dell\\\\AI Assignment\\\\ARG_4.csv')\n",
    "\n",
    "# Create an empty DataFrame\n",
    "errors = pd.DataFrame()\n",
    "\n",
    "# Adding a columns named \"Model\" and \"MSE\" to the errors \n",
    "errors[\"Model\"] = [\"Autoregression\", \"Moving Average\", \"ARIMA\"]\n",
    "errors[\"MSE\"] = [ar_score, ma_score, arima_score]\n",
    "\n",
    "# Sorts the rows in the errors DataFrame by the \"MSE\" column in ascending order\n",
    "errors = errors.sort_values(\"MSE\", ascending = True, ignore_index = True)\n",
    "\n",
    "# Sets the row index labels of the errors DataFrame to be the values in the \"Model\" column\n",
    "errors.index = errors.Model\n",
    "\n",
    "# Removes the \"Model\" column from the errors DataFrame\n",
    "del errors[\"Model\"]\n",
    "\n",
    "# Applies a background color to each cell in s \n",
    "def coloring_bg(s, min_, max_, cmap='Blues', low=0, high=0):\n",
    "    color_range = max_ - min_\n",
    "    norm = colors.Normalize(min_ - (color_range * low), max_ + (color_range * high)) #maps the data range between min_ and max_ to the (0, 1) color range\n",
    "    normed = norm(s.values) #normalize data to a specific range used for color mapping; s.values, a numpy array of values that we want to normalize\n",
    "    c = [colors.rgb2hex(x) for x in plt.cm.get_cmap(cmap)(normed)] #convert normalized data values into a list of hexadecimal color codes that can be used to color a plot\n",
    "    return ['background-color: %s' % color for color in c]\n",
    "errors.style.apply(coloring_bg, min_ = errors.min().min(),\n",
    "               max_ = errors.max().max(), low = 0.1, high = 0.85)\n",
    "\n",
    "#Moving Average (MA) model appears to have the lowest error or the best fit for the data, followed by the ARIMA model, and then the Autoregression (AR) model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8119cdd0",
   "metadata": {},
   "source": [
    "# Prediction and Reverse Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f2442e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013    0.009411\n",
      "2014    0.001830\n",
      "2015    0.014594\n",
      "2016    0.011421\n",
      "2017    0.013700\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#len(indexed_data_log_scaled)-10 is the index of the 10th last value in the logarithmic transformation\n",
    "\n",
    "#indexed_data.iloc[len(indexed_data_log_scaled)-10:] is a slice of the indexed_data DataFrame\n",
    "#that includes all rows starting from the 10th last value\n",
    "\n",
    "#.index.values returns an array of datetime values\n",
    "\n",
    "test_indexed_time = list(indexed_data.iloc[len(indexed_data_log_scaled)-10:].index.values)\n",
    "\n",
    "#test_indexed_time is a list of datetime values for the test set, created earlier\n",
    "predictions_ARIMA_diff = pd.Series(predictions, test_indexed_time, copy=True)\n",
    "print(predictions_ARIMA_diff.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0bf052",
   "metadata": {},
   "source": [
    "# Convert to Cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "74da5ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013    0.009411\n",
      "2014    0.011241\n",
      "2015    0.025835\n",
      "2016    0.037256\n",
      "2017    0.050956\n",
      "2018    0.063840\n",
      "2019    0.077173\n",
      "2020    0.090320\n",
      "2021    0.103558\n",
      "2022    0.116756\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#cumsum() computes the cumulative sum of the differences predicted by the ARIMA model\n",
    "\n",
    "predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()\n",
    "print(predictions_ARIMA_diff_cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "92a5b70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    0.648757\n",
       "2014    0.604538\n",
       "2015    0.528267\n",
       "2016    0.737286\n",
       "2017    0.775351\n",
       "dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_ARIMA_log = pd.Series(indexed_data_log_scaled['Value'].iloc[len(indexed_data_log_scaled)-10:], index=test_indexed_time)\n",
    "predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum, fill_value=0)\n",
    "predictions_ARIMA_log.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c49d11",
   "metadata": {},
   "source": [
    "# Inverse of log is exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0fedf6a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23a64f630a0>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To obtain the predictions in the original scale\n",
    "\n",
    "predictions_ARIMA = np.exp(predictions_ARIMA_log)\n",
    "plt.plot(indexed_data['Value'].iloc[len(indexed_data_log_scaled)-10:])\n",
    "plt.plot(predictions_ARIMA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da8223f",
   "metadata": {},
   "source": [
    "# Forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab574d5f",
   "metadata": {},
   "source": [
    "# Fit ARIMA model\n",
    "model = ARIMA(train, order=(2, 0, 0)) # Example order, may need to be tuned\n",
    "model_fit = model.fit()\n",
    "\n",
    "# Make predictions for future year(s)\n",
    "future_year = 2023 # Example future year\n",
    "predictions = model_fit.forecast(steps=1)[0] # Example number of steps\n",
    "\n",
    "# Print predicted value\n",
    "print(f'Predicted value for {future_year}: {predictions[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b87133e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ARIMA_model.plot_predict(1,len(indexed_data)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe01c069",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4ef831b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}