SARIMA for Variability Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0fa0de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sms\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.ar_model import AutoReg\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d532053e",
   "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>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sm variable  value\n",
       "0  7463.0      pm3   0.00\n",
       "1  7576.0      pm3   0.00\n",
       "2  7576.0      pm3   0.00\n",
       "3  7463.0      pm3   0.00\n",
       "4  7463.0      pm3   2.51"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('user2_data.csv')\n",
    "df.drop(['ttime','pm1','pm2','am','lum','temp','humd','pres'],axis=1,inplace=True)\n",
    "\n",
    "#df.columns = df.columns[[0]].tolist() + list(range(1,2))\n",
    "s = df.melt('sm')\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9298f379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sm          0\n",
       "variable    0\n",
       "value       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45edd7ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20166 entries, 0 to 20165\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   sm        20166 non-null  float64\n",
      " 1   variable  20166 non-null  object \n",
      " 2   value     20166 non-null  float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 472.8+ KB\n"
     ]
    }
   ],
   "source": [
    "s.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd83f7a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "31334a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "88c9760d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adf_test(series):\n",
    "    result=adfuller(series)\n",
    "    print('ADF Statistics: {}'.format(result[0]))\n",
    "    print('p- value: {}'.format(result[1]))\n",
    "    if result[1] <= 0.05:\n",
    "        print(\"strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5487bf1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistics: -2.6007571323979657\n",
      "p- value: 0.09284088451858502\n",
      "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
     ]
    }
   ],
   "source": [
    "adf_test(s['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b632838",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Use Techniques Differencing\n",
    "s['Moisture First Difference']=s['value']-s['value'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9dd11d67",
   "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>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sm variable  value  Moisture First Difference\n",
       "0  7463.0      pm3   0.00                        NaN\n",
       "1  7576.0      pm3   0.00                       0.00\n",
       "2  7576.0      pm3   0.00                       0.00\n",
       "3  7463.0      pm3   0.00                       0.00\n",
       "4  7463.0      pm3   2.51                       2.51"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c80a6972",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistics: -9.07368533649762\n",
      "p- value: 4.2350676763739835e-15\n",
      "strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(s['Moisture First Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8c10bc6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Use Techniques Differencing\n",
    "s['Moisture Second Difference']=s['Moisture First Difference']-s['Moisture First Difference'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40ebf2aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistics: -28.21558021819986\n",
      "p- value: 0.0\n",
      "strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(s['Moisture Second Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67d483db",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 8 months \n",
    "## Use Techniques Differencing\n",
    "s['Moisture 8 Difference']=s['value']-s['value'].shift(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "811d1a28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistics: -9.20548443052271\n",
      "p- value: 1.9501285966009887e-15\n",
      "strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(s['Moisture 8 Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ebb905a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1071b3bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf = plot_acf(s[\"Moisture Second Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ec86f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf12 = plot_acf(s[\"Moisture 8 Difference\"].dropna())\n",
    "pacf12 = plot_pacf(s[\"Moisture 8 Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a378cfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = plot_pacf(s[\"Moisture Second Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62367b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacf12 = plot_pacf(s[\"Moisture 8 Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e21a81db",
   "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>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "      <th>Moisture Second Difference</th>\n",
       "      <th>Moisture 8 Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.51</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20165</th>\n",
       "      <td>328.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20166 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sm variable  value  Moisture First Difference  \\\n",
       "0      7463.0      pm3   0.00                        NaN   \n",
       "1      7576.0      pm3   0.00                       0.00   \n",
       "2      7576.0      pm3   0.00                       0.00   \n",
       "3      7463.0      pm3   0.00                       0.00   \n",
       "4      7463.0      pm3   2.51                       2.51   \n",
       "...       ...      ...    ...                        ...   \n",
       "20161   327.0      pm3  27.38                       0.00   \n",
       "20162   327.0      pm3  27.38                       0.00   \n",
       "20163   327.0      pm3  27.38                       0.00   \n",
       "20164   327.0      pm3  27.93                       0.55   \n",
       "20165   328.0      pm3  27.93                       0.00   \n",
       "\n",
       "       Moisture Second Difference  Moisture 8 Difference  \n",
       "0                             NaN                    NaN  \n",
       "1                             NaN                    NaN  \n",
       "2                            0.00                    NaN  \n",
       "3                            0.00                    NaN  \n",
       "4                            2.51                    NaN  \n",
       "...                           ...                    ...  \n",
       "20161                        0.00                  -0.55  \n",
       "20162                        0.00                  -0.55  \n",
       "20163                        0.00                  -0.55  \n",
       "20164                        0.55                   0.00  \n",
       "20165                       -0.55                   0.00  \n",
       "\n",
       "[20166 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### split train and test data\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f8a1ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime,timedelta\n",
    "train_dataset_end=15124\n",
    "test_dataset_end=20165"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f4012f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=s[:train_dataset_end]\n",
    "test_data=s[train_dataset_end:test_dataset_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f15a58e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "##prediction\n",
    "pred_start_date=test_data.index[0]\n",
    "pred_end_date=test_data.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e0317954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "      <th>Moisture Second Difference</th>\n",
       "      <th>Moisture 8 Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15124</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15125</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15126</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15127</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15128</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20160</th>\n",
       "      <td>326.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5041 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          sm variable  value  Moisture First Difference  \\\n",
       "15124  469.0      pm3  37.43                       0.00   \n",
       "15125  469.0      pm3  37.43                       0.00   \n",
       "15126  468.0      pm3  37.43                       0.00   \n",
       "15127  468.0      pm3  37.43                       0.00   \n",
       "15128  468.0      pm3  37.43                       0.00   \n",
       "...      ...      ...    ...                        ...   \n",
       "20160  326.0      pm3  27.38                       0.00   \n",
       "20161  327.0      pm3  27.38                       0.00   \n",
       "20162  327.0      pm3  27.38                       0.00   \n",
       "20163  327.0      pm3  27.38                       0.00   \n",
       "20164  327.0      pm3  27.93                       0.55   \n",
       "\n",
       "       Moisture Second Difference  Moisture 8 Difference  \n",
       "15124                        0.00                  -0.56  \n",
       "15125                        0.00                  -0.56  \n",
       "15126                        0.00                  -0.56  \n",
       "15127                        0.00                   0.00  \n",
       "15128                        0.00                   0.00  \n",
       "...                           ...                    ...  \n",
       "20160                        0.55                  -0.55  \n",
       "20161                        0.00                  -0.55  \n",
       "20162                        0.00                  -0.55  \n",
       "20163                        0.00                  -0.55  \n",
       "20164                        0.55                   0.00  \n",
       "\n",
       "[5041 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f9dfdc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>value</td>      <th>  No. Observations:  </th>   <td>15124</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(5, 2, 0)</td>  <th>  Log Likelihood     </th> <td>-4836.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 24 Mar 2023</td> <th>  AIC                </th> <td>9685.564</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>11:48:15</td>     <th>  BIC                </th> <td>9731.308</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>9700.735</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td> - 15124</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.8113</td> <td>    0.003</td> <td> -312.870</td> <td> 0.000</td> <td>   -0.816</td> <td>   -0.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.6260</td> <td>    0.003</td> <td> -183.794</td> <td> 0.000</td> <td>   -0.633</td> <td>   -0.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.4694</td> <td>    0.004</td> <td> -122.010</td> <td> 0.000</td> <td>   -0.477</td> <td>   -0.462</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>  <td>   -0.3222</td> <td>    0.004</td> <td>  -87.422</td> <td> 0.000</td> <td>   -0.329</td> <td>   -0.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>  <td>   -0.1567</td> <td>    0.003</td> <td>  -52.449</td> <td> 0.000</td> <td>   -0.163</td> <td>   -0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.1110</td> <td>    0.000</td> <td>  954.593</td> <td> 0.000</td> <td>    0.111</td> <td>    0.111</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>10.76</td> <th>  Jarque-Bera (JB):  </th> <td>36352182.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th>    <td>0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.16</td>  <th>  Skew:              </th>    <td>11.49</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>   <td>242.09</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  value   No. Observations:                15124\n",
       "Model:                 ARIMA(5, 2, 0)   Log Likelihood               -4836.782\n",
       "Date:                Fri, 24 Mar 2023   AIC                           9685.564\n",
       "Time:                        11:48:15   BIC                           9731.308\n",
       "Sample:                             0   HQIC                          9700.735\n",
       "                              - 15124                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.8113      0.003   -312.870      0.000      -0.816      -0.806\n",
       "ar.L2         -0.6260      0.003   -183.794      0.000      -0.633      -0.619\n",
       "ar.L3         -0.4694      0.004   -122.010      0.000      -0.477      -0.462\n",
       "ar.L4         -0.3222      0.004    -87.422      0.000      -0.329      -0.315\n",
       "ar.L5         -0.1567      0.003    -52.449      0.000      -0.163      -0.151\n",
       "sigma2         0.1110      0.000    954.593      0.000       0.111       0.111\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                  10.76   Jarque-Bera (JB):          36352182.27\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.16   Skew:                            11.49\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                       242.09\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## create a ARIMA model\n",
    "model_ARIMA = sms.tsa.arima.ARIMA(train_data['value'], order=(5,2,0))\n",
    "model_Arima_fit = model_ARIMA.fit()\n",
    "model_Arima_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7bae577b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "      <th>Moisture Second Difference</th>\n",
       "      <th>Moisture 8 Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.51</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15119</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15120</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15121</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15122</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15123</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15124 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sm variable  value  Moisture First Difference  \\\n",
       "0      7463.0      pm3   0.00                        NaN   \n",
       "1      7576.0      pm3   0.00                       0.00   \n",
       "2      7576.0      pm3   0.00                       0.00   \n",
       "3      7463.0      pm3   0.00                       0.00   \n",
       "4      7463.0      pm3   2.51                       2.51   \n",
       "...       ...      ...    ...                        ...   \n",
       "15119   469.0      pm3  37.43                      -0.56   \n",
       "15120   469.0      pm3  37.43                       0.00   \n",
       "15121   469.0      pm3  37.43                       0.00   \n",
       "15122   469.0      pm3  37.43                       0.00   \n",
       "15123   469.0      pm3  37.43                       0.00   \n",
       "\n",
       "       Moisture Second Difference  Moisture 8 Difference  \n",
       "0                             NaN                    NaN  \n",
       "1                             NaN                    NaN  \n",
       "2                            0.00                    NaN  \n",
       "3                            0.00                    NaN  \n",
       "4                            2.51                    NaN  \n",
       "...                           ...                    ...  \n",
       "15119                       -0.56                  -0.56  \n",
       "15120                        0.56                  -0.56  \n",
       "15121                        0.00                  -0.56  \n",
       "15122                        0.00                  -0.56  \n",
       "15123                        0.00                  -0.56  \n",
       "\n",
       "[15124 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b7f4a46a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ARIMA=sms.tsa.arima.ARIMA(train_data['value'],order=(0,2,23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0357c2fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Users\\sarth\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "model_Arima_fit=model_ARIMA.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "58b9e11c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>value</td>      <th>  No. Observations:  </th>   <td>15124</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(0, 2, 23)</td> <th>  Log Likelihood     </th>  <td>403.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 24 Mar 2023</td> <th>  AIC                </th> <td>-759.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>11:54:07</td>     <th>  BIC                </th> <td>-576.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>-698.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                     <td> - 15124</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -1.1945</td> <td>    0.003</td> <td> -372.304</td> <td> 0.000</td> <td>   -1.201</td> <td>   -1.188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>    0.1323</td> <td>    0.006</td> <td>   21.347</td> <td> 0.000</td> <td>    0.120</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>  <td>   -0.1189</td> <td>    0.008</td> <td>  -15.429</td> <td> 0.000</td> <td>   -0.134</td> <td>   -0.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4</th>  <td>   -0.0551</td> <td>    0.007</td> <td>   -7.835</td> <td> 0.000</td> <td>   -0.069</td> <td>   -0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5</th>  <td>    0.1220</td> <td>    0.007</td> <td>   18.681</td> <td> 0.000</td> <td>    0.109</td> <td>    0.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L6</th>  <td>    0.1285</td> <td>    0.008</td> <td>   16.739</td> <td> 0.000</td> <td>    0.113</td> <td>    0.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L7</th>  <td>    0.0124</td> <td>    0.007</td> <td>    1.810</td> <td> 0.070</td> <td>   -0.001</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L8</th>  <td>    0.0682</td> <td>    0.005</td> <td>   13.132</td> <td> 0.000</td> <td>    0.058</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L9</th>  <td>   -0.0953</td> <td>    0.005</td> <td>  -19.079</td> <td> 0.000</td> <td>   -0.105</td> <td>   -0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L10</th> <td>   -0.0525</td> <td>    0.006</td> <td>   -8.841</td> <td> 0.000</td> <td>   -0.064</td> <td>   -0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L11</th> <td>    0.1467</td> <td>    0.006</td> <td>   24.401</td> <td> 0.000</td> <td>    0.135</td> <td>    0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L12</th> <td>    0.3483</td> <td>    0.003</td> <td>  101.552</td> <td> 0.000</td> <td>    0.342</td> <td>    0.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L13</th> <td>   -0.4951</td> <td>    0.005</td> <td> -104.653</td> <td> 0.000</td> <td>   -0.504</td> <td>   -0.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L14</th> <td>    0.1739</td> <td>    0.005</td> <td>   32.376</td> <td> 0.000</td> <td>    0.163</td> <td>    0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L15</th> <td>   -0.1326</td> <td>    0.006</td> <td>  -21.249</td> <td> 0.000</td> <td>   -0.145</td> <td>   -0.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L16</th> <td>   -0.2100</td> <td>    0.006</td> <td>  -34.717</td> <td> 0.000</td> <td>   -0.222</td> <td>   -0.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L17</th> <td>    0.1714</td> <td>    0.006</td> <td>   30.381</td> <td> 0.000</td> <td>    0.160</td> <td>    0.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L18</th> <td>   -0.0479</td> <td>    0.007</td> <td>   -7.279</td> <td> 0.000</td> <td>   -0.061</td> <td>   -0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L19</th> <td>    0.2262</td> <td>    0.006</td> <td>   37.092</td> <td> 0.000</td> <td>    0.214</td> <td>    0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L20</th> <td>    0.1572</td> <td>    0.005</td> <td>   28.693</td> <td> 0.000</td> <td>    0.146</td> <td>    0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L21</th> <td>   -0.2928</td> <td>    0.005</td> <td>  -54.425</td> <td> 0.000</td> <td>   -0.303</td> <td>   -0.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L22</th> <td>   -0.0765</td> <td>    0.007</td> <td>  -10.788</td> <td> 0.000</td> <td>   -0.090</td> <td>   -0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L23</th> <td>    0.1129</td> <td>    0.005</td> <td>   24.564</td> <td> 0.000</td> <td>    0.104</td> <td>    0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0554</td> <td>    0.000</td> <td>  351.296</td> <td> 0.000</td> <td>    0.055</td> <td>    0.056</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.03</td> <th>  Jarque-Bera (JB):  </th> <td>5693531.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.86</td> <th>  Prob(JB):          </th>    <td>0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.26</td> <th>  Skew:              </th>    <td>4.55</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>    <td>97.62</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  value   No. Observations:                15124\n",
       "Model:                ARIMA(0, 2, 23)   Log Likelihood                 403.652\n",
       "Date:                Fri, 24 Mar 2023   AIC                           -759.303\n",
       "Time:                        11:54:07   BIC                           -576.330\n",
       "Sample:                             0   HQIC                          -698.619\n",
       "                              - 15124                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -1.1945      0.003   -372.304      0.000      -1.201      -1.188\n",
       "ma.L2          0.1323      0.006     21.347      0.000       0.120       0.144\n",
       "ma.L3         -0.1189      0.008    -15.429      0.000      -0.134      -0.104\n",
       "ma.L4         -0.0551      0.007     -7.835      0.000      -0.069      -0.041\n",
       "ma.L5          0.1220      0.007     18.681      0.000       0.109       0.135\n",
       "ma.L6          0.1285      0.008     16.739      0.000       0.113       0.144\n",
       "ma.L7          0.0124      0.007      1.810      0.070      -0.001       0.026\n",
       "ma.L8          0.0682      0.005     13.132      0.000       0.058       0.078\n",
       "ma.L9         -0.0953      0.005    -19.079      0.000      -0.105      -0.085\n",
       "ma.L10        -0.0525      0.006     -8.841      0.000      -0.064      -0.041\n",
       "ma.L11         0.1467      0.006     24.401      0.000       0.135       0.159\n",
       "ma.L12         0.3483      0.003    101.552      0.000       0.342       0.355\n",
       "ma.L13        -0.4951      0.005   -104.653      0.000      -0.504      -0.486\n",
       "ma.L14         0.1739      0.005     32.376      0.000       0.163       0.184\n",
       "ma.L15        -0.1326      0.006    -21.249      0.000      -0.145      -0.120\n",
       "ma.L16        -0.2100      0.006    -34.717      0.000      -0.222      -0.198\n",
       "ma.L17         0.1714      0.006     30.381      0.000       0.160       0.182\n",
       "ma.L18        -0.0479      0.007     -7.279      0.000      -0.061      -0.035\n",
       "ma.L19         0.2262      0.006     37.092      0.000       0.214       0.238\n",
       "ma.L20         0.1572      0.005     28.693      0.000       0.146       0.168\n",
       "ma.L21        -0.2928      0.005    -54.425      0.000      -0.303      -0.282\n",
       "ma.L22        -0.0765      0.007    -10.788      0.000      -0.090      -0.063\n",
       "ma.L23         0.1129      0.005     24.564      0.000       0.104       0.122\n",
       "sigma2         0.0554      0.000    351.296      0.000       0.055       0.056\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):           5693531.21\n",
       "Prob(Q):                              0.86   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.26   Skew:                             4.55\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        97.62\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Arima_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ffe6a4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "      <th>Moisture Second Difference</th>\n",
       "      <th>Moisture 8 Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15124</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15125</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15126</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15127</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15128</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20160</th>\n",
       "      <td>326.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5041 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          sm variable  value  Moisture First Difference  \\\n",
       "15124  469.0      pm3  37.43                       0.00   \n",
       "15125  469.0      pm3  37.43                       0.00   \n",
       "15126  468.0      pm3  37.43                       0.00   \n",
       "15127  468.0      pm3  37.43                       0.00   \n",
       "15128  468.0      pm3  37.43                       0.00   \n",
       "...      ...      ...    ...                        ...   \n",
       "20160  326.0      pm3  27.38                       0.00   \n",
       "20161  327.0      pm3  27.38                       0.00   \n",
       "20162  327.0      pm3  27.38                       0.00   \n",
       "20163  327.0      pm3  27.38                       0.00   \n",
       "20164  327.0      pm3  27.93                       0.55   \n",
       "\n",
       "       Moisture Second Difference  Moisture 8 Difference  \n",
       "15124                        0.00                  -0.56  \n",
       "15125                        0.00                  -0.56  \n",
       "15126                        0.00                  -0.56  \n",
       "15127                        0.00                   0.00  \n",
       "15128                        0.00                   0.00  \n",
       "...                           ...                    ...  \n",
       "20160                        0.55                  -0.55  \n",
       "20161                        0.00                  -0.55  \n",
       "20162                        0.00                  -0.55  \n",
       "20163                        0.00                  -0.55  \n",
       "20164                        0.55                   0.00  \n",
       "\n",
       "[5041 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "54ab81f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=model_Arima_fit.predict(start=pred_start_date,end=pred_end_date)\n",
    "residuals=test_data['value']-pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f84a6a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15124     37.484285\n",
       "15125     37.564775\n",
       "15126     37.619121\n",
       "15127     37.564508\n",
       "15128     37.551099\n",
       "            ...    \n",
       "20160   -123.249469\n",
       "20161   -123.281404\n",
       "20162   -123.313339\n",
       "20163   -123.345274\n",
       "20164   -123.377209\n",
       "Name: predicted_mean, Length: 5041, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c057759e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15124     -0.054285\n",
       "15125     -0.134775\n",
       "15126     -0.189121\n",
       "15127     -0.134508\n",
       "15128     -0.121099\n",
       "            ...    \n",
       "20160    150.629469\n",
       "20161    150.661404\n",
       "20162    150.693339\n",
       "20163    150.725274\n",
       "20164    151.307209\n",
       "Length: 5041, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1b760c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Density'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_Arima_fit.resid.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cf4e08a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Temp\\ipykernel_12860\\95659616.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_data['Predicted_ARIMA']=pred\n"
     ]
    }
   ],
   "source": [
    "test_data['Predicted_ARIMA']=pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "218ef4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data[['value','Predicted_ARIMA']].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aa962999",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CBQ6Vz8nJUf/+/fXHP/5R2dnZmjBhgh588EGtX7/eVmblypVKSUnRjBkztH37dnXp0kVJSUmXvM05AABoOOrtKiqLxaK1a9dqwIABVZaZPHmyPvzwQ7u7jQ4dOlT5+flKT0+X9Ovv2dx00016+eWXJf16d9Tw8HA98sgjmjJlilPXAQAAeAa3utFfZmamEhMT7aYlJSVpwoQJkqRz584pKytLU6dOtc338vJSYmJihTt6XqykpMTuRwvLf423RYsWNbq1OQAAcB3DMHT69GmFhYVd8sabbhVwcnNzFRISYjctJCREhYWFOnv2rE6dOqXS0tJKy5T/VkxlUlNTNXPmTKe0GQAA1K+DBw+qdevW1ZZxq4DjLFOnTlVKSorteUFBgdq0aaODBw/K39+/1vX/Y8P/05Kv9qu0rOLZPm8vi0beEqHHbr+m1ssBAKAhKywsVHh4uK688spLlnWrgBMaGqq8vDy7aXl5efL395efn5+8vb3l7e1daZnQ0NAq67VarXa/zFzO39+/TgLO8B7Xaum2PHlVMprJYpFG9LhW/v5Na70cAAAgh4aXuNV9cBISEpSRkWE3bcOGDUpISJAk+fj4KCYmxq5MWVmZMjIybGVcIbJlU80Z2FleF21vb4tFXhZpzsDOimhJuAEAoD45tQenqKhIe/futT3PyclRdna2mjdvrjZt2mjq1Kn6+eef9eabb0qS/vrXv+rll1/WpEmT9MADD2jjxo1atWqVPvzwQ1sdKSkpGjFihGJjYxUXF6d58+apuLhYo0aNcuaqXNKg2HB1uspffV/6QpI0qnuE7o9vS7gBAMAFnBpwtm3bpj/+8Y+25+XjYEaMGKElS5boyJEjOnDggG1+ZGSkPvzwQz322GN66aWX1Lp1a73++utKSkqylRkyZIiOHTum6dOnKzc3V127dlV6enqFgceu0LbFb2Em5fZr1MTHrc4AAgDQYDTIXxMvLCxUQECACgoK6mQMTrkz5y4oevqvNyX8YVYSAQcAgDpUk7/fbjUGBwAAoC4QcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOk0cnUDGqKc48Vate2gDp06q9bN/DQ4NlyRLZu6ulkAAJgGAaeerdp2UFPW7JDFYpFhGLJYLHp18z7NGdhZg2LDXd08AABMgVNU9SjneLGmrNmhMkMqLTPs/p28Zof2Hy92dRMBADAFAk49WrXtoCwWS6XzLBaLVm47WM8tAgDAnAg49ejQqbMyDKPSeYZh6NCps/XcIgAAzImAU49aN/OrtgendTO/em4RAADmRMCpR4Njw6vtwRnCIGMAAOoEAaceRbZsqjkDO8vrok4cb4tFXhZpzsDOiuBScQAA6gSXidezQbHh6nSVv/q+9IUkaVT3CN0f35ZwAwBAHSLguEDbFr+FmZTbr1ETH3YDAAB1iVNUAADAdAg4AADAdAg4AADAdAg4AADAdAg4AADAdAg4AADAdAg4AADAdAg4AADAdOol4CxYsEARERHy9fVVfHy8tm7dWmXZnj17ymKxVHj079/fVmbkyJEV5vfp06c+VgUAAHgAp99Cd+XKlUpJSVFaWpri4+M1b948JSUlaffu3QoODq5Q/p133tG5c+dsz0+cOKEuXbpo0KBBduX69OmjxYsX255brVbnrQQAAPAoTu/BmTt3rsaMGaNRo0YpOjpaaWlpatKkiRYtWlRp+ebNmys0NNT22LBhg5o0aVIh4FitVrtyzZo1c/aqAAAAD+HUgHPu3DllZWUpMTHxtwV6eSkxMVGZmZkO1fHGG29o6NChatrU/scoN23apODgYHXo0EHjxo3TiRMnqqyjpKREhYWFdg8AAGBeTg04x48fV2lpqUJCQuymh4SEKDc395Kv37p1q7777js9+OCDdtP79OmjN998UxkZGZozZ442b96svn37qrS0tNJ6UlNTFRAQYHuEh4df/koBAAC359Y/Y/3GG2/o+uuvV1xcnN30oUOH2v5//fXXq3PnzmrXrp02bdqkXr16Vahn6tSpSklJsT0vLCwk5AAAYGJO7cFp2bKlvL29lZeXZzc9Ly9PoaGh1b62uLhYK1as0OjRoy+5nKioKLVs2VJ79+6tdL7VapW/v7/dAwAAmJdTA46Pj49iYmKUkZFhm1ZWVqaMjAwlJCRU+9rVq1erpKRE999//yWXc+jQIZ04cUKtWrWqdZsBAIDnc/pVVCkpKXrttde0dOlS7dy5U+PGjVNxcbFGjRolSRo+fLimTp1a4XVvvPGGBgwYoBYtWthNLyoq0hNPPKGvv/5a+/fvV0ZGhu6++261b99eSUlJzl4dAADgAZw+BmfIkCE6duyYpk+frtzcXHXt2lXp6em2gccHDhyQl5d9ztq9e7e++OILffzxxxXq8/b21o4dO7R06VLl5+crLCxMvXv31uzZs7kXDgAAkFRPg4yTk5OVnJxc6bxNmzZVmNahQwcZhlFpeT8/P61fv74umwcAAEyG36ICAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmQ8ABAACmUy93MgbqWs7xYq3adlCHTp1V62Z+GhwbrsiWTV3dLACAmyDgwOOs2nZQU9bskMVikWEYslgsenXzPs0Z2FmDYsNd3TwAgBvgFBU8Ss7xYk1Zs0NlhlRaZtj9O3nNDu0/XuzqJgIA3AABBx5l1baDslgslc6zWCxaue1gPbcIAOCOCDjwKIdOna3yl+YNw9ChU2fruUUAAHdEwIFHad3Mr9oenNbN/Oq5RQAAd0TAgUcZHBtebQ/OEAYZAwBEwIGHiWzZVHMGdpbXRZ043haLvCzSnIGdFcGl4gAAcZk4PNCg2HB1uspffV/6QpI0qnuE7o9vS7gBANgQcOCR2rb4Lcyk3H6NmvjwVgYA/IZTVAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHS4eQjQQOUcL9aqbQd16NRZtW7mp8Gx4YrkZokATIKAAzRAq7Yd1JQ1O2SxWGQYhiwWi17dvE9zBnbWIH7PC4AJcIoKaGByjhdrypodKjOk0jLD7t/Ja3Zo//FiVzcRAGqNgAM0MKu2HZTFYql0nsVi0cptB+u5RQBQ9wg4QANz6NRZGYZR6TzDMHTo1Nl6bhEA1D0CDtDAtG7mV20PTutmfvXcIgCoewQcoIEZHBtebQ/OEAYZAzABAg7QwES2bKo5AzvL66JOHG+LRV4Wac7AzorgUnEAJsBl4kADNCg2XJ2u8lffl76QJI3qHqH749sSbgCYRr304CxYsEARERHy9fVVfHy8tm7dWmXZJUuWyGKx2D18fX3tyhiGoenTp6tVq1by8/NTYmKi9uzZ4+zVAEylbYvfwkzK7dcQbgCYitMDzsqVK5WSkqIZM2Zo+/bt6tKli5KSknT06NEqX+Pv768jR47YHj/99JPd/Oeff17z589XWlqatmzZoqZNmyopKUm//PKLs1cHAAB4AKcHnLlz52rMmDEaNWqUoqOjlZaWpiZNmmjRokVVvsZisSg0NNT2CAkJsc0zDEPz5s3TU089pbvvvludO3fWm2++qcOHD+vdd9919uoAAAAP4NSAc+7cOWVlZSkxMfG3BXp5KTExUZmZmVW+rqioSG3btlV4eLjuvvtuff/997Z5OTk5ys3NtaszICBA8fHxVdZZUlKiwsJCuwcAADAvpwac48ePq7S01K4HRpJCQkKUm5tb6Ws6dOigRYsW6b333tNbb72lsrIy3XLLLTp06JAk2V5XkzpTU1MVEBBge4SHcxksAABm5naXiSckJGj48OHq2rWrevTooXfeeUdBQUF69dVXL7vOqVOnqqCgwPY4eJBb0QMAYGZODTgtW7aUt7e38vLy7Kbn5eUpNDTUoToaN26sG264QXv37pUk2+tqUqfVapW/v7/dAwAAmJdTA46Pj49iYmKUkZFhm1ZWVqaMjAwlJCQ4VEdpaam+/fZbtWrVSpIUGRmp0NBQuzoLCwu1ZcsWh+sEAADm5vQb/aWkpGjEiBGKjY1VXFyc5s2bp+LiYo0aNUqSNHz4cF111VVKTU2VJM2aNUs333yz2rdvr/z8fL3wwgv66aef9OCDD0r69QqrCRMm6JlnntHVV1+tyMhITZs2TWFhYRowYICzVwcAAHgApwecIUOG6NixY5o+fbpyc3PVtWtXpaen2wYJHzhwQF5ev3UknTp1SmPGjFFubq6aNWummJgYffXVV4qOjraVmTRpkoqLizV27Fjl5+ere/fuSk9Pr3BDQAAA0DDVy081JCcnKzk5udJ5mzZtsnv+j3/8Q//4xz+qrc9isWjWrFmaNWtWXTURAACYiNtdRQUAAFBbBBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA69RJwFixYoIiICPn6+io+Pl5bt26tsuxrr72mW2+9Vc2aNVOzZs2UmJhYofzIkSNlsVjsHn369HH2agAAAA/h9ICzcuVKpaSkaMaMGdq+fbu6dOmipKQkHT16tNLymzZt0n333adPP/1UmZmZCg8PV+/evfXzzz/blevTp4+OHDlie/zrX/9y9qo0GDnHizUnfZce+df/ak76LuUcL3Z1kwAAqJFGzl7A3LlzNWbMGI0aNUqSlJaWpg8//FCLFi3SlClTKpRftmyZ3fPXX39da9asUUZGhoYPH26bbrVaFRoa6tzGN0Crth3UlDU7ZLFYZBiGLBaLXt28T3MGdtag2HBXNw8AAIc4tQfn3LlzysrKUmJi4m8L9PJSYmKiMjMzHarjzJkzOn/+vJo3b243fdOmTQoODlaHDh00btw4nThxoso6SkpKVFhYaPdARTnHizVlzQ6VGVJpmWH37+Q1O7SfnhwAgIdwag/O8ePHVVpaqpCQELvpISEh2rVrl0N1TJ48WWFhYXYhqU+fPvrTn/6kyMhI7du3T08++aT69u2rzMxMeXt7V6gjNTVVM2fOrN3KNACrth2UxWKRDKPCPIvFopXbDmpyn44uaBlQN3KOF2vVtoM6dOqsWjfz0+DYcEW2bOrqZjVo7BM4i9NPUdXGc889pxUrVmjTpk3y9fW1TR86dKjt/9dff706d+6sdu3aadOmTerVq1eFeqZOnaqUlBTb88LCQoWHc7rl9w6dOiujknAjSYZh6NCps/XcIqDucPrV/bBP4ExOPUXVsmVLeXt7Ky8vz256Xl7eJcfPvPjii3ruuef08ccfq3PnztWWjYqKUsuWLbV3795K51utVvn7+9s9UFHrZn6/9uBUwmKxqHUzv3puEVA3OP3qftgncDanBhwfHx/FxMQoIyPDNq2srEwZGRlKSEio8nXPP/+8Zs+erfT0dMXGxl5yOYcOHdKJEyfUqlWrOml3QzU4NrzaHpwhfKOCh7Kdfq1E+elX1C/2CZzN6ZeJp6Sk6LXXXtPSpUu1c+dOjRs3TsXFxbarqoYPH66pU6fays+ZM0fTpk3TokWLFBERodzcXOXm5qqoqEiSVFRUpCeeeEJff/219u/fr4yMDN19991q3769kpKSnL06phbZsqnmDOwsr4uOOd4Wi7ws0pyBnRXBeXF4KE6/uh/2CZzN6WNwhgwZomPHjmn69OnKzc1V165dlZ6ebht4fODAAXl5/ZazXnnlFZ07d0733nuvXT0zZszQ008/LW9vb+3YsUNLly5Vfn6+wsLC1Lt3b82ePVtWq9XZq2N6g2LD1ekqf/V96QtJ0qjuEbo/vi3hBh7Ndvq1igH0nH6tf+wTOFu9DDJOTk5WcnJypfM2bdpk93z//v3V1uXn56f169fXUctQmbYtfgszKbdfoyY+bj0WHbikwbHhenXzvkrncfrVNdgncDZ+iwqA6XH61f2wT+BsfDUH0CBw+tX9sE/gTAQcAA0Gp1/dD/sEzsIpKgAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoMV4ep5Rwv1qptB3Xo1Fm1buanwbHhiuQSVAAwPQIOTGvVtoOasmaHLBaLDMOQxWLRq5v3ac7AzhrEXVIBwNQ4RQVTyjlerClrdqjMkErLDLt/J6/Zof3Hi13dRACAExFwYEqrth389Yf8KmGxWLRy28F6bhEAoD5xigqmdOjUWRmV/Eqx9OsP+R06dfay6mVMDwB3w3GpcgQcmFLrZn6/9uBUEnIsFotaN/OrcZ2M6QHgbjguVY1TVDClwbHh1fbgDKnhB58xPQDcDcel6hFwYEqRLZtqzsDO8rpoGI63xSIvizRnYOca/1oxY3oAuBuOS9XjFBVMa1BsuDpd5a++L30hSRrVPUL3x7etcbiRnDemBwAuF8el6hFwYGptW/wWZlJuv0ZNfC7vLe+MMT2eggGMgHtqyMclRxBwAAcMjg3Xq5v3VTrvcsb0eAoGMALuy1nHJbN8qWEMDuCAuh7T4wkYwAi4N2ccl1ZtO6hef9+khZ/9qA93HNbCz35Ur79v0moPHM9DDw7goLoc0+MJbAMYq+j+XrntoCb36eiClgEoV5Pj0qV6Zi7+UmP73P/fv5PX7NBNEc096nhHwAHkeJdsXY3p8QQMYAQ8gyPHJUdON9f0S427n8oy79EZcBDjTCrHAEbAHBztmanJlxpPOG4yBgcNGuNMqlbXN0sE4BqO3i/H9qWminLlX2o85bhJwEGDxo2yqtYQB1bDfeUcL9ac9F165F//qznpu5TjJn9EPYGjPTOOfqnxlOMmp6jQoDHOpHoNbWA13JMnnA5xZ46ebi7/UjO5/HSWfv1SY8iw+1LjKcdNenDQoDnaJduQ/X4AI+EG9clTToe4s5qcbh4UG64PH+1uez6qe4Q2TuxpFyQ95bhJwEGDxjgTwL15yukQd1bT082X+lLjKcdNAg4aNMaZAO7NU06HuDtHemYc5SnHTcbgoMFjnAngvrhdQd2py/t4ecJxkx4cQIwzAdyVp5wOaYjc/bhJwAEAuC1POR0C98MpKrgVd7/1N4D65wmnQ5yB42HtEHDgNrjXBYCqeMLvwNVlIOF4WHvu9w5Bg2S2X7FF/XLVN12+YaNcXQYSjod1g4ADt1DTX7EFyrnqmy7fsFGurgMJx8O6US+DjBcsWKCIiAj5+voqPj5eW7durbb86tWr1bFjR/n6+ur666/XRx99ZDffMAxNnz5drVq1kp+fnxITE7Vnzx5nrgKcjHtdeD5X/FaQq+5yW9Pl8jtK5lbXNyPkeFg3nN6Ds3LlSqWkpCgtLU3x8fGaN2+ekpKStHv3bgUHB1co/9VXX+m+++5Tamqq7rjjDi1fvlwDBgzQ9u3b1alTJ0nS888/r/nz52vp0qWKjIzUtGnTlJSUpB9++EG+vr4Ot+3MuQtqdO5Cna3rmYvqOvO7erf8eNL2/5Lzpbb/f7b7mKyNveusDXXBFe0rLSu75PxPdx21PXe0ja4q5wnqcp0/33NMi7/aL4skQ5JFUtrmfXrglkh1v7plHbf8N6uzqv/D8fz6XRoU81tvSl3tv5os11XbxlOY4bO3/adTKqsikJQZhrb/dMru+HUpZjkexkc1r2YtLs/v/7ZWx2JUFRPrSHx8vG666Sa9/PLLkqSysjKFh4frkUce0ZQpUyqUHzJkiIqLi7Vu3TrbtJtvvlldu3ZVWlqaDMNQWFiYJk6cqMcff1ySVFBQoJCQEC1ZskRDhw6tUGdJSYlKSkpszwsLCxUeHq7wCavkZW1S16sMAACcoKzkjA7OG6yCggL5+/tXW9app6jOnTunrKwsJSYm/rZALy8lJiYqMzOz0tdkZmbalZekpKQkW/mcnBzl5ubalQkICFB8fHyVdaampiogIMD2CA/n/DgAAGbm1FNUx48fV2lpqUJCQuymh4SEaNeuXZW+Jjc3t9Lyubm5tvnl06oq83tTp05VSkqK7Xl5D87W/6/XJRNgXbn4FJWjSs6X6q/LtkuS0obdWG03oSPlDpwo1owPfpAkJUWHqGfHYIX6O35Krzb1OVo2r/AXfbbnmE4UnVOLK3z0h6uDFFKLNrqKI/skt/AXPbn228rGEcpikVLvuV4h/r4Ol6vJsutS2uZ92rr/ZJXti4torr/2aFfj9tXlNqypSy3b0eW6cttINfuM1vWy6/t9WNPl1vW2+WLPcS36KsfuVKQhVXoq0tE2evrx0BmnqAoLC9VqnmNlG8RVVFarVVartcL0Jj6N6u1eCr61/HBbG3s7VEdV5TbtPqqFn/9oe75hZ54+3pmnh/4QpR7XVBwLdSk1qa8mZdu2aKq/tDDX5Y9V7ZPMfSdsB8Hfs0j6at8J3RfXxuFyNVl2XQrx9622fSH+vlW2obbv64gWTfXQH6L06mc/VvjD8tAfouzunXK5Klu2o8t15bapzWe+tsu+3HJ1rT63TWJ0iDpdFaBPdx/VsaISBV1h1R87BCs0oPpAUt228fTjoTP+vl6oQZ1O/evesmVLeXt7Ky8vz256Xl6eQkNDK31NaGhoteXL/83Ly1OrVq3synTt2rUOW1+3Etq1qPFrLh5MFR/VvMo3y6XK5Rwv1muf/2j3LbLs//6/8LMfNfSmNjW6hLEm9dX1sj2FI/tu+dYD1dZh6Nf3jaPlarLsuhQa4Kt1Ow5XOf/3v1FTV+/rcgntWmjoTW208qL70QyJDa/V+8qRZTuy3Jpumx+OFNj+/+W+4xoW37bS++o44zPv6PauqzY6iyu3jSTdc+NVtW4j6oZTx+D4+PgoJiZGGRkZtmllZWXKyMhQQkJCpa9JSEiwKy9JGzZssJWPjIxUaGioXZnCwkJt2bKlyjoburq+hLEm9dX1ss3E9ivJlbj4V5IdLecqF/9WkLeXxe7f+vqtoIiWTTW5T0f9874bNLlPx3oLzZdabk22zaptB3XH/C9szxd/sV+9/r5Jqy/jM3I5n7v9J367dH3uhv9X6aXsddlGV+GY1HA4PTampKRoxIgRio2NVVxcnObNm6fi4mKNGjVKkjR8+HBdddVVSk1NlST97W9/U48ePfT3v/9d/fv314oVK7Rt2zYtXLhQ0q9vwAkTJuiZZ57R1VdfbbtMPCwsTAMGDHD26nikur6nQk3q434OVRscG65XN++rdN7Fv5LsaDlXGhQbrpsimtdpL4pZOLJt7G4U939Ka3GjuJp+7spvWlhu8Rf7teiLHLubFtZ1G12FY1LD4fSAM2TIEB07dkzTp09Xbm6uunbtqvT0dNsg4QMHDsjL67eOpFtuuUXLly/XU089pSeffFJXX3213n33Xds9cCRp0qRJKi4u1tixY5Wfn6/u3bsrPT29RvfAaUhsPQBV3BWzpj0ANamvrpdtJuXf7if/7m64hmHYfbt3tJyrlfdmXMrvewqqOsVhJpfaNnV959qafO4cDS5mubsux6SGo15O/CUnJys5ObnSeZs2baowbdCgQRo0aFCV9VksFs2aNUuzZs2qqyaaWl33ANSkPk/ofXAlR3s+zNJD4khPQUNU170KNfncORpczNLzwTGp4aiXn2qAa9X1GIma1OcO4zPcnaPjR1w1zqSuVNVT4OyfVfAENR1ndanxMjX53DkaXNx9LJijOCY1HAzdNgFHuvzrugegJvWZpfcBtVPTUxwN6VRWTXtcHOkFc/Rz5+gpGzP1fHBMahgIOB6uJl3+jo6RcFRN6qvrZcPz1OQUR0M7leXoOKuaDvR15HPnaHDxlLFgjnLlMakhhXdXIuC4sUt9CMxyVQMaBkd7Chrq+9qRXgVnDPStSXCh56P2Glp4dyUCjpty5ENglqsa0DA42lPQkN/Xl+pVcNZA35oEF3pjL19DDe+uwiBjN+ToYEyzXNWAhsHRwZ28r6vmzIG+nj6I3RNwk8H6RcBxQ45+CMxyVQMajkGx4do4safG/iFK/TuHaewforRxYk+7rnne11UbHBtebfjzpIG+zuLIHZldhfBevwg4bsjRDwEHO3iiS/UU8L6uGpc4V8/df0qC8F6/CDhuyNEPAQc7mBHv6+o50gvWEHnCfZYI7/WLQcZuqCb3m+CqBvyeGS5BdfX72t23oScM9K3vbegJg9PNdqm9uyPguKGafgg84WCH+mGmS1Bd9b420zZ0FVdsQ08Z3+Lq8N6QEHDcFB8C1BSXoNYe27D2XLUNPelHNPlSWj8Yg+PGuGwTNcElqLXHNqw9V21Dxrfg9wg4gEl4She9O2Mb1p6rtiGD0/F7nKICTMKTuujdFduw9ly5DTm1j4vRgwOYBF30tcc2rD1Xb0NO7aMcAQdwAlfcTZUu+tpjG9aeGbehO98dGVWzGFVFbRMrLCxUQECACgoK5O/v7+rmwGTKL5Etv4rE22KRIaPeLjPef7yYLvpaYhvWnlm2oas/z7BXk7/fBBwCDupQzvFi9fr7JrtLZMt5WaSNE3t65EEeaIj4PLufmvz95hQVUIe4zBgwDz7Pno2AA9QhLjMGzIPPs2cj4AB1iF8LBsyDz7NnI+AAdcjVl8gCqDt8nj0bAQeoQ2a8RBZoqPg8ezauouIqKjiBWS6RBcDn2Z1wmfglEHAAAPA8XCYOAAAaNAIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHacGnJMnT2rYsGHy9/dXYGCgRo8eraKiomrLP/LII+rQoYP8/PzUpk0bPfrooyooKLArZ7FYKjxWrFjhzFUBAAAepJEzKx82bJiOHDmiDRs26Pz58xo1apTGjh2r5cuXV1r+8OHDOnz4sF588UVFR0frp59+0l//+lcdPnxYb7/9tl3ZxYsXq0+fPrbngYGBzlwVAADgQZz2W1Q7d+5UdHS0vvnmG8XGxkqS0tPT1a9fPx06dEhhYWEO1bN69Wrdf//9Ki4uVqNGv+Yxi8WitWvXasCAAZfVNn6LCgAAz+MWv0WVmZmpwMBAW7iRpMTERHl5eWnLli0O11O+EuXhptz48ePVsmVLxcXFadGiRaoup5WUlKiwsNDuAQAAzMtpp6hyc3MVHBxsv7BGjdS8eXPl5uY6VMfx48c1e/ZsjR071m76rFmzdNttt6lJkyb6+OOP9fDDD6uoqEiPPvpopfWkpqZq5syZl7ciAADA49S4B2fKlCmVDvK9+LFr165aN6ywsFD9+/dXdHS0nn76abt506ZNU7du3XTDDTdo8uTJmjRpkl544YUq65o6daoKCgpsj4MHD9a6fQAAwH3VuAdn4sSJGjlyZLVloqKiFBoaqqNHj9pNv3Dhgk6ePKnQ0NBqX3/69Gn16dNHV155pdauXavGjRtXWz4+Pl6zZ89WSUmJrFZrhflWq7XS6QAAwJxqHHCCgoIUFBR0yXIJCQnKz89XVlaWYmJiJEkbN25UWVmZ4uPjq3xdYWGhkpKSZLVa9f7778vX1/eSy8rOzlazZs0IMQAAQJITx+Bce+216tOnj8aMGaO0tDSdP39eycnJGjp0qO0Kqp9//lm9evXSm2++qbi4OBUWFqp37946c+aM3nrrLbsBwUFBQfL29tYHH3ygvLw83XzzzfL19dWGDRv07LPP6vHHH3fWqgAAAA/j1PvgLFu2TMnJyerVq5e8vLw0cOBAzZ8/3zb//Pnz2r17t86cOSNJ2r59u+0Kq/bt29vVlZOTo4iICDVu3FgLFizQY489JsMw1L59e82dO1djxoxx5qoAAAAP4rT74Lgz7oMDAIDncYv74AAAALgKAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJgOAQcAAJiOUwPOyZMnNWzYMPn7+yswMFCjR49WUVFRta/p2bOnLBaL3eOvf/2rXZkDBw6of//+atKkiYKDg/XEE0/owoULzlwVAADgQRo5s/Jhw4bpyJEj2rBhg86fP69Ro0Zp7NixWr58ebWvGzNmjGbNmmV73qRJE9v/S0tL1b9/f4WGhuqrr77SkSNHNHz4cDVu3FjPPvus09YFAAB4DothGIYzKt65c6eio6P1zTffKDY2VpKUnp6ufv366dChQwoLC6v0dT179lTXrl01b968Suf/+9//1h133KHDhw8rJCREkpSWlqbJkyfr2LFj8vHxuWTbCgsLFRAQoIKCAvn7+1/eCgIAgHpVk7/fTjtFlZmZqcDAQFu4kaTExER5eXlpy5Yt1b522bJlatmypTp16qSpU6fqzJkzdvVef/31tnAjSUlJSSosLNT3339faX0lJSUqLCy0ewAAAPNy2imq3NxcBQcH2y+sUSM1b95cubm5Vb7uz3/+s9q2bauwsDDt2LFDkydP1u7du/XOO+/Y6r043EiyPa+q3tTUVM2cObM2qwMAADxIjQPOlClTNGfOnGrL7Ny587IbNHbsWNv/r7/+erVq1Uq9evXSvn371K5du8uqc+rUqUpJSbE9LywsVHh4+GW3EQAAuLcaB5yJEydq5MiR1ZaJiopSaGiojh49ajf9woULOnnypEJDQx1eXnx8vCRp7969ateunUJDQ7V161a7Mnl5eZJUZb1Wq1VWq9XhZQIAAM9W44ATFBSkoKCgS5ZLSEhQfn6+srKyFBMTI0nauHGjysrKbKHFEdnZ2ZKkVq1a2er9r//6Lx09etR2CmzDhg3y9/dXdHR0DdcGAACYkdMGGV977bXq06ePxowZo61bt+rLL79UcnKyhg4daruC6ueff1bHjh1tPTL79u3T7NmzlZWVpf379+v999/X8OHD9Yc//EGdO3eWJPXu3VvR0dH6y1/+ov/85z9av369nnrqKY0fP55eGgAAIMnJN/pbtmyZOnbsqF69eqlfv37q3r27Fi5caJt//vx57d6923aVlI+Pjz755BP17t1bHTt21MSJEzVw4EB98MEHttd4e3tr3bp18vb2VkJCgu6//34NHz7c7r45AACgYXPafXDcGffBAQDA87jFfXAAAABchYADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMh4ADAABMx6kB5+TJkxo2bJj8/f0VGBio0aNHq6ioqMry+/fvl8ViqfSxevVqW7nK5q9YscKZqwIAADxII2dWPmzYMB05ckQbNmzQ+fPnNWrUKI0dO1bLly+vtHx4eLiOHDliN23hwoV64YUX1LdvX7vpixcvVp8+fWzPAwMD67z9AADAMzkt4OzcuVPp6en65ptvFBsbK0n65z//qX79+unFF19UWFhYhdd4e3srNDTUbtratWs1ePBgXXHFFXbTAwMDK5QFAACQnHiKKjMzU4GBgbZwI0mJiYny8vLSli1bHKojKytL2dnZGj16dIV548ePV8uWLRUXF6dFixbJMIwq6ykpKVFhYaHdAwAAmJfTenByc3MVHBxsv7BGjdS8eXPl5uY6VMcbb7yha6+9Vrfccovd9FmzZum2225TkyZN9PHHH+vhhx9WUVGRHn300UrrSU1N1cyZMy9vRQAAgMepcQ/OlClTqhwIXP7YtWtXrRt29uxZLV++vNLem2nTpqlbt2664YYbNHnyZE2aNEkvvPBClXVNnTpVBQUFtsfBgwdr3T4AAOC+atyDM3HiRI0cObLaMlFRUQoNDdXRo0ftpl+4cEEnT550aOzM22+/rTNnzmj48OGXLBsfH6/Zs2erpKREVqu1wnyr1VrpdAAAYE41DjhBQUEKCgq6ZLmEhATl5+crKytLMTExkqSNGzeqrKxM8fHxl3z9G2+8obvuusuhZWVnZ6tZs2aEGAAAIMmJY3CuvfZa9enTR2PGjFFaWprOnz+v5ORkDR061HYF1c8//6xevXrpzTffVFxcnO21e/fu1WeffaaPPvqoQr0ffPCB8vLydPPNN8vX11cbNmzQs88+q8cff9xZqwIAADyMU++Ds2zZMiUnJ6tXr17y8vLSwIEDNX/+fNv88+fPa/fu3Tpz5ozd6xYtWqTWrVurd+/eFeps3LixFixYoMcee0yGYah9+/aaO3euxowZ48xVAQAAHsRiVHd9tUkVFhYqICBABQUF8vf3d3VzAACAA2ry95vfogIAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKZDwAEAAKbjtIDzX//1X7rlllvUpEkTBQYGOvQawzA0ffp0tWrVSn5+fkpMTNSePXvsypw8eVLDhg2Tv7+/AgMDNXr0aBUVFTlhDQAAgKdyWsA5d+6cBg0apHHjxjn8mueff17z589XWlqatmzZoqZNmyopKUm//PKLrcywYcP0/fffa8OGDVq3bp0+++wzjR071hmrAAAAPJTFMAzDmQtYsmSJJkyYoPz8/GrLGYahsLAwTZw4UY8//rgkqaCgQCEhIVqyZImGDh2qnTt3Kjo6Wt98841iY2MlSenp6erXr58OHTqksLAwh9pUWFiogIAAFRQUyN/fv1brBwAA6kdN/n43qqc2XVJOTo5yc3OVmJhomxYQEKD4+HhlZmZq6NChyszMVGBgoC3cSFJiYqK8vLy0ZcsW3XPPPZXWXVJSopKSEtvzgoICSb9uKAAA4BnK/2470jfjNgEnNzdXkhQSEmI3PSQkxDYvNzdXwcHBdvMbNWqk5s2b28pUJjU1VTNnzqwwPTw8vLbNBgAA9ez06dMKCAiotkyNAs6UKVM0Z86casvs3LlTHTt2rEm1Tjd16lSlpKTYnpeVlenkyZNq0aKFLBZLnS6rsLBQ4eHhOnjwIKe/3AT7xP2wT9wT+8X9sE/sGYah06dPOzQkpUYBZ+LEiRo5cmS1ZaKiompSpU1oaKgkKS8vT61atbJNz8vLU9euXW1ljh49ave6Cxcu6OTJk7bXV8ZqtcpqtdpNc/TKrsvl7+/Pm9HNsE/cD/vEPbFf3A/75DeX6rkpV6OAExQUpKCgoMtq0KVERkYqNDRUGRkZtkBTWFioLVu22K7ESkhIUH5+vrKyshQTEyNJ2rhxo8rKyhQfH++UdgEAAM/jtMvEDxw4oOzsbB04cEClpaXKzs5Wdna23T1rOnbsqLVr10qSLBaLJkyYoGeeeUbvv/++vv32Ww0fPlxhYWEaMGCAJOnaa69Vnz59NGbMGG3dulVffvmlkpOTNXToUIevoAIAAObntEHG06dP19KlS23Pb7jhBknSp59+qp49e0qSdu/ebbuiSZImTZqk4uJijR07Vvn5+erevbvS09Pl6+trK7Ns2TIlJyerV69e8vLy0sCBAzV//nxnrUaNWa1WzZgxo8IpMbgO+8T9sE/cE/vF/bBPLp/T74MDAABQ3/gtKgAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEnDq0YMECRUREyNfXV/Hx8dq6daurm9SgfPbZZ7rzzjsVFhYmi8Wid999126+YRiaPn26WrVqJT8/PyUmJmrPnj2uaWwDkZqaqptuuklXXnmlgoODNWDAAO3evduuzC+//KLx48erRYsWuuKKKzRw4EDl5eW5qMXm98orr6hz5862O+MmJCTo3//+t20++8P1nnvuOdu94cqxX2qOgFNHVq5cqZSUFM2YMUPbt29Xly5dlJSUVOGnJeA8xcXF6tKlixYsWFDp/Oeff17z589XWlqatmzZoqZNmyopKUm//PJLPbe04di8ebPGjx+vr7/+Whs2bND58+fVu3dvFRcX28o89thj+uCDD7R69Wpt3rxZhw8f1p/+9CcXttrcWrdureeee05ZWVnatm2bbrvtNt199936/vvvJbE/XO2bb77Rq6++qs6dO9tNZ79cBgN1Ii4uzhg/frzteWlpqREWFmakpqa6sFUNlyRj7dq1tudlZWVGaGio8cILL9im5efnG1ar1fjXv/7lghY2TEePHjUkGZs3bzYM49d90LhxY2P16tW2Mjt37jQkGZmZma5qZoPTrFkz4/XXX2d/uNjp06eNq6++2tiwYYPRo0cP429/+5thGHxOLhc9OHXg3LlzysrKUmJiom2al5eXEhMTlZmZ6cKWoVxOTo5yc3Pt9lFAQIDi4+PZR/Wo/M7lzZs3lyRlZWXp/PnzdvulY8eOatOmDfulHpSWlmrFihUqLi5WQkIC+8PFxo8fr/79+9ttf4nPyeVy2k81NCTHjx9XaWmpQkJC7KaHhIRo165dLmoVLpabmytJle6j8nlwrrKyMk2YMEHdunVTp06dJP26X3x8fBQYGGhXlv3iXN9++60SEhL0yy+/6IorrtDatWsVHR2t7Oxs9oeLrFixQtu3b9c333xTYR6fk8tDwAFQL8aPH6/vvvtOX3zxhaub0uB16NBB2dnZKigo0Ntvv60RI0Zo8+bNrm5Wg3Xw4EH97W9/04YNG+x+exG1wymqOtCyZUt5e3tXGNGel5en0NBQF7UKFyvfD+wj10hOTta6dev06aefqnXr1rbpoaGhOnfunPLz8+3Ks1+cy8fHR+3bt1dMTIxSU1PVpUsXvfTSS+wPF8nKytLRo0d14403qlGjRmrUqJE2b96s+fPnq1GjRgoJCWG/XAYCTh3w8fFRTEyMMjIybNPKysqUkZGhhIQEF7YM5SIjIxUaGmq3jwoLC7Vlyxb2kRMZhqHk5GStXbtWGzduVGRkpN38mJgYNW7c2G6/7N69WwcOHGC/1KOysjKVlJSwP1ykV69e+vbbb5WdnW17xMbGatiwYbb/s19qjlNUdSQlJUUjRoxQbGys4uLiNG/ePBUXF2vUqFGublqDUVRUpL1799qe5+TkKDs7W82bN1ebNm00YcIEPfPMM7r66qsVGRmpadOmKSwsTAMGDHBdo01u/PjxWr58ud577z1deeWVtvECAQEB8vPzU0BAgEaPHq2UlBQ1b95c/v7+euSRR5SQkKCbb77Zxa03p6lTp6pv375q06aNTp8+reXLl2vTpk1av349+8NFrrzyStu4tHJNmzZVixYtbNPZL5fB1Zdxmck///lPo02bNoaPj48RFxdnfP31165uUoPy6aefGpIqPEaMGGEYxq+Xik+bNs0ICQkxrFar0atXL2P37t2ubbTJVbY/JBmLFy+2lTl79qzx8MMPG82aNTOaNGli3HPPPcaRI0dc12iTe+CBB4y2bdsaPj4+RlBQkNGrVy/j448/ts1nf7iHiy8TNwz2y+WwGIZhuChbAQAAOAVjcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOkQcAAAgOn8/2Bv6zCuq05TAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf8 = plot_acf(s[\"Moisture 8 Difference\"].dropna())\n",
    "pacf8 = plot_pacf(s[\"Moisture 8 Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "86230e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "## create a SARIMA model\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1da12cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_SARIMA=SARIMAX(train_data['value'],order=(1,0,3),seasonal_order=(1,1,1,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "19dcd73a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "model_SARIMA_fit=model_SARIMA.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1864abb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                 <td>value</td>              <th>  No. Observations:  </th>   <td>15124</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 3)x(1, 1, [1], 12)</td> <th>  Log Likelihood     </th> <td>1555.436</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Fri, 24 Mar 2023</td>         <th>  AIC                </th> <td>-3096.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>12:02:15</td>             <th>  BIC                </th> <td>-3043.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                          <td>0</td>                <th>  HQIC               </th> <td>-3079.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                             <td> - 15124</td>             <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.9994</td> <td>    0.000</td> <td> 7172.842</td> <td> 0.000</td> <td>    0.999</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.1847</td> <td>    0.002</td> <td> -108.120</td> <td> 0.000</td> <td>   -0.188</td> <td>   -0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>    <td>   -0.0588</td> <td>    0.005</td> <td>  -11.164</td> <td> 0.000</td> <td>   -0.069</td> <td>   -0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>    <td>   -0.1450</td> <td>    0.005</td> <td>  -31.911</td> <td> 0.000</td> <td>   -0.154</td> <td>   -0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>    0.0670</td> <td>    0.003</td> <td>   26.226</td> <td> 0.000</td> <td>    0.062</td> <td>    0.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.6577</td> <td>    0.002</td> <td> -350.032</td> <td> 0.000</td> <td>   -0.661</td> <td>   -0.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>    0.0477</td> <td>  9.2e-05</td> <td>  517.993</td> <td> 0.000</td> <td>    0.047</td> <td>    0.048</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>3.53</td> <th>  Jarque-Bera (JB):  </th> <td>15464402.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.06</td> <th>  Prob(JB):          </th>    <td>0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.56</td> <th>  Skew:              </th>    <td>-0.47</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>159.71</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                       \n",
       "============================================================================================\n",
       "Dep. Variable:                                value   No. Observations:                15124\n",
       "Model:             SARIMAX(1, 0, 3)x(1, 1, [1], 12)   Log Likelihood                1555.436\n",
       "Date:                              Fri, 24 Mar 2023   AIC                          -3096.873\n",
       "Time:                                      12:02:15   BIC                          -3043.510\n",
       "Sample:                                           0   HQIC                         -3079.174\n",
       "                                            - 15124                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.9994      0.000   7172.842      0.000       0.999       1.000\n",
       "ma.L1         -0.1847      0.002   -108.120      0.000      -0.188      -0.181\n",
       "ma.L2         -0.0588      0.005    -11.164      0.000      -0.069      -0.048\n",
       "ma.L3         -0.1450      0.005    -31.911      0.000      -0.154      -0.136\n",
       "ar.S.L12       0.0670      0.003     26.226      0.000       0.062       0.072\n",
       "ma.S.L12      -0.6577      0.002   -350.032      0.000      -0.661      -0.654\n",
       "sigma2         0.0477    9.2e-05    517.993      0.000       0.047       0.048\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   3.53   Jarque-Bera (JB):          15464402.30\n",
       "Prob(Q):                              0.06   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.56   Skew:                            -0.47\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                       159.71\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_SARIMA_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b8fc9aa",
   "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>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "      <th>Moisture Second Difference</th>\n",
       "      <th>Moisture 8 Difference</th>\n",
       "      <th>Predicted_ARIMA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20160</th>\n",
       "      <td>326.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.249469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.281404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.313339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.345274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-123.377209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sm variable  value  Moisture First Difference  \\\n",
       "20160  326.0      pm3  27.38                       0.00   \n",
       "20161  327.0      pm3  27.38                       0.00   \n",
       "20162  327.0      pm3  27.38                       0.00   \n",
       "20163  327.0      pm3  27.38                       0.00   \n",
       "20164  327.0      pm3  27.93                       0.55   \n",
       "\n",
       "       Moisture Second Difference  Moisture 8 Difference  Predicted_ARIMA  \n",
       "20160                        0.55                  -0.55      -123.249469  \n",
       "20161                        0.00                  -0.55      -123.281404  \n",
       "20162                        0.00                  -0.55      -123.313339  \n",
       "20163                        0.00                  -0.55      -123.345274  \n",
       "20164                        0.55                   0.00      -123.377209  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1f412af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_Sarima=model_SARIMA_fit.predict(start=15125,end=20164)\n",
    "residuals=test_data['value']-pred_Sarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7d168cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_SARIMA_fit.resid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6e9bf0c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Density'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IESNUVlamadOmSQp2V6644opuLRBAZulwjgodFQAJJHUy7T/90z/p6quv1uHDh1VaWhp+fObMmbrlllu6rTgAmYcj9AF0RVJBRZIKCwtVWFgY9djUqVPPuyAAma1DA4WkAiCBpIJKc3OzVqxYoY0bN+ro0aMKBAJRn9+/f3+3FAcg85BTAHRFUkHlq1/9qt544w3deeedKioqCh+tDwCdab/00/5IfQCIlFRQeemll/Q///M/mj59enfXAyDDdThHhZwCIIGkdv0MGDBAAwcO7O5aAFwAOpxMy+IPgASSCio/+tGPtGzZsqjr/QCALXRUAHRBUks/jz76qPbt26eCggKNGDFCvXr1ivr8jh07bH2fNWvWaM2aNaqurpYkjR8/XsuWLdOcOXOSKQtAGmCYFkBXJBVU5s2b1y0vPnToUK1YsUKjR4+WZVl68sknNXfuXL3zzjsaP358t7wGgNTSYXiWpAIgAZeVYsdCDhw4UI888oi+8pWvdPrcxsZGeb1eNTQ0cHQ/kCbqTpzW51f8OXw/2+3S3oeud7AiAKZ15e/vpGZUJOnEiRP61a9+pYqKCh0/flxScMnn0KFDSX0/v9+v9evXq7m5OXwkP4DMw9IPgK5Iaunnvffe06xZs+T1elVdXa27775bAwcO1HPPPaeamhqtW7fO9vfatWuXpk2bpjNnzqhfv356/vnnNW7cuJjP9fl88vl84fuNjY3JlA/AQYEAV08GYF9SHZXy8nItWrRIe/bsUW5ubvjx66+/Xps2berS9xozZox27typrVu36p577tHChQv1t7/9LeZzKysr5fV6w7eSkpJkygeQQogpABJJKqhs27ZNX//61zs8fvHFF6u+vr5L3ysnJ0ejRo3SlClTVFlZqdLSUj322GMxn1tRUaGGhobwrba2NpnyATio49WTnakDQHpIaunH4/HEXHb56KOPNHjw4PMqKBAIRC3vtH9dj8dzXt8fgLNiHfBmWRaX4gAQU1IdlZtvvlkPPPCAzp49K0lyuVyqqanR9773PX3pS1+y/X0qKiq0adMmVVdXa9euXaqoqNDrr7+uBQsWJFMWgDTQ/gh9ia4KgPiSCiqPPvqompqaNHjwYJ0+fVpf/OIXNWrUKOXl5enBBx+0/X2OHj2qu+66S2PGjNHMmTO1bds2vfLKK/qHf/iHZMoCkAZiDc+SUwDEk9TSj9fr1YYNG/TXv/5V7777rpqamjR58mTNmjWrS9/n17/+dTIvDyCNxe6oWJJY+gHQUZeDSiAQUFVVlZ577jlVV1fL5XJp5MiRKiwsZJ0ZQKdCHRW361xooaMCIJ4uLf1YlqWbb75ZX/3qV3Xo0CFNnDhR48eP18GDB7Vo0SLdcsstPVUngAwRCiVZ7nP/qOlwrD4AtOlSR6WqqkqbNm3Sxo0bNWPGjKjP/fnPf9a8efO0bt063XXXXd1aJIDMEQh3VFwKxRZyCoB4utRRefrpp/Vv//ZvHUKKJP393/+9vv/97+upp57qtuIAZJ5AIPjfyI4KAMTTpaDy3nvv6R//8R/jfn7OnDl69913z7soAJkr1FHJiphno6MCIJ4uBZXjx4+roKAg7ucLCgr02WefnXdRADJXKJS4IzoqsQ6BAwCpi0HF7/crOzv+WEtWVpZaW1vPuygAmSvUUcmOGqZ1qhoAqa5Lw7SWZWnRokVxj7GPd/Q9AISEh2kjOyqs/QCIo0tBZeHChZ0+hx0/ABIJdU+iZlQcqgVA6utSUFm7dm1P1QHggnHuwLfwIyQVAHEkda0fAEhWIMYwLS0VAPEQVAAYFQh0HKZl1w+AeAgqAIyK1VFh1w+AeAgqAIyyYh74RlIBEBtBBYBR4V0/bnb9AOgcQQWAUaFzVFwcoQ/ABoIKAKNCmcTtkkJZhWFaAPEQVAAYFT6Z1uVSqKdCRwVAPAQVAEZZ1rkD30LLPwQVAPEQVAAYFQgE/+uK7Kiw9AMgDoIKAKMCUR2V4GN0VADEQ1ABYFT4wDeX69zSj4P1AEhtBBUARlnh7ckKL/0EOJoWQBwEFQBGhSKJy+VSxFEqABATQQWAUVEzKmLXD4DECCoAjIqeUQl+zK4fAPEQVAAYZXHgG4AuIKgAMCoQMUzrZtcPgE4QVAAYFTrwzR2x7SdASwVAHAQVAEYFYmxPJqcAiIegAsAoK8aBbyz+AIiHoALAqNAOH47QB2AHQQWAUaHtyS6Xi2FaAJ0iqAAwKvrAt+jHAKA9ggoAo2Ie+EZOARAHQQWAUZEHvokj9AF0gqACwKjQlZJdkcO0TKkAiIOgAsCo6GHa4Md0VADEQ1ABYFQok3D1ZAB2EFQAGBU5o+LmCH0AnSCoADAq6qKEbUnFT1ABEIejQaWyslJXXXWV8vLyNGTIEM2bN0+7d+92siQAPSxye3JWW1AJDdgCQHuOBpU33nhDS5Ys0ZYtW7RhwwadPXtW1113nZqbm50sC0APijzwLatt2w85BUA82U6++Msvvxx1v6qqSkOGDNH27dv1hS98waGqAPQkK8aBb36SCoA4UmpGpaGhQZI0cOBAhysB0FMiz1EJL/0wowIgDkc7KpECgYDuu+8+TZ8+XRMmTIj5HJ/PJ5/PF77f2NhoqjwA3SQUSSIvSkhHBUA8KdNRWbJkid5//32tX78+7nMqKyvl9XrDt5KSEoMVAugOUTMq7PoB0ImUCCr33nuv/vSnP+m1117T0KFD4z6voqJCDQ0N4Vttba3BKgF0h1i7fiyCCoA4HF36sSxL3/jGN/T888/r9ddf18iRIxM+3+PxyOPxGKoOQE+IPvAttPTjZEUAUpmjQWXJkiX63e9+pz/+8Y/Ky8tTfX29JMnr9ap3795Olgagh0Qe+BZe+mFGBUAcji79rFmzRg0NDbr22mtVVFQUvj3zzDNOlgWgB0Uu/XCEPoDOOL70A+DCEu6oSOz6AdCplBimBXDhCB/45nZxjgqAThFUABhlxZhRIagAiIegAsCo6BkVdv0ASIygAsCoWAe+cfVkAPEQVAAYZcXY9cPJtADiIagAMOrcOSpc6wdA5wgqAIyKufRDRwVAHAQVAEaFmicuueRmRgVAJwgqAIyyIjsqoaUfcgqAOAgqAIxqbUsl2Vludv0A6BRBBYBRocHZbHfEMC0zKgDiIKgAMKq1LahkuSO2J9NRARAHQQWAUeGOSpaLpR8AnSKoADCqNRA8Lz/LHbHrh5wCIA6CCgCjImdUsphRAdAJggoAo87NqLDrB0DnCCoAjGLXD4CuIKgAMCp0jkrkrh86KgDiIagAMMofsT2Za/0A6AxBBYBRsXb9+ANOVgQglRFUABgVa9cPHRUA8RBUABgVGpyN7qgQVADERlABYFT4ooRuN+eoAOgUQQWAUdHDtMHH2PUDIB6CCgCjIq/142JGBUAnCCoAjGqNsT2ZXT8A4iGoADCKXT8AuoKgAsCoyHNUQh2VVmZUAMRBUAFg1LmOilu9soO/gs62svYDIDaCCgCjImdUPG3bfloYUgEQB0EFgFF+/7kZlV7ZwaWfswQVAHEQVAAYFdlRycnKkiT5WPoBEAdBBYBRkeeo5LTNqLQQVADEQVABYFR414+LoAKgcwQVAMYEApZCO5GDSz8M0wJIjKACwJjIiw9mu93KaRumpaMCIB6CCgBj/BEHu2VlnRumZdcPgHgIKgCMiTyBNtvNjAqAzhFUABgTGUhystwEFQCdIqgAMCYUSLLdLrkjOio+ln4AxEFQAWBMKKiEAkqvrHPDtBZXUAYQg6NBZdOmTbrppptUXFwsl8ulF154wclyAPSw0DbkXm3bkj1tw7SSdNZPUAHQkaNBpbm5WaWlpVq9erWTZQAwpH1HJfRfiZ0/AGLLdvLF58yZozlz5jhZAgCDQh2V0EFvkUGlpTWgvh5HygKQwphRAWBMqKPiaQsoWW6XstzBORUuTAggFkc7Kl3l8/nk8/nC9xsbGx2sBkBXhYJKaEZFknKz3Wpu8cvX6neqLAApLK06KpWVlfJ6veFbSUmJ0yUB6ILQHErkkk/vnOBA7emzBBUAHaVVUKmoqFBDQ0P4Vltb63RJALrA19oxqHiyg0HlzFmWfgB0lFZLPx6PRx4P03ZAumo/TCtJub2CH59uoaMCoCNHg0pTU5P27t0bvn/gwAHt3LlTAwcO1LBhwxysDEBPCM+oxFj6OcOMCoAYHA0qb7/9tmbMmBG+X15eLklauHChqqqqHKoKQE85G6ujElr6oaMCIAZHg8q1117LsdnABaT99mSJjgqAxNJqmBZAemt/Mq3EMC2AxAgqAIw5d60fV/ix8PZkln4AxEBQAWBMrI5KbtvHLP0AiIWgAsCYc9uTz101ObcXw7QA4iOoADAmVkfl3DAtMyoAOiKoADAmHFQiZlRCSz/MqACIhaACwJhY1/rJDXVUuNYPgBgIKgCMiT1My0UJAcRHUAFgjC/GybThGRXOUQEQA0EFgDHnOiqRu36Cv4Z8bE8GEANBBYAxZ2Mc+BZe+mGYFkAMBBUAxsScUeFaPwASIKgAMCbWRQnpqABIhKACwJiWGNuTGaYFkAhBBYAxoY5Kr4hdP6FhWs5RARALQQWAMS2xtif34sA3APERVAAYE3OYtte5A98sy3KkLgCpi6ACwJhYQaVP24xKwJJ8XJgQQDsEFQDGxFr66ZuTHf64yddqvCYAqY2gAsCYWB0Vt9ulfp5gWGk6Q1ABEI2gAsAIy7LCFx4MbUkO6esJ3qejAqA9ggoAI3ytAYVmZftELPdICndUTtJRAdAOQQWAEaciTp4NbUkO6ZfbSxIdFQAdEVQAGHGqJRhCcrLdynK7oj6XF5pR8Z01XheA1EZQAWBE6Fo+fdrNp0himBZAXAQVAEaEB2l7dQwq+b2DQeWzU3RUAEQjqAAwIjSj0n7HjyQVeXtLkg43nDZaE4DUR1ABYESipZ/i/rmSpLoTZ4zWBCD1EVQAGBHqqPTpld3hc8X9gx2VuhN0VABEI6gAMCI0o5KbYOmn7sRpLkwIIApBBYARJ88EB2X7eeIv/TS3+NXIzh8AEQgqAIz4tKlFknRRP0+Hz/XJyVb/PsFD3xioBRCJoALAiE+bfZKkQX07BhVJKvYypwKgI4IKgG4VCFh6p+YznTjVEvX4sbaOyqB+OTG/7uIBwaDy8WcEFQDnEFQAdKuHX/5Qt/znm5q3+q9R1+75tCnYUbkoTlAZNrCPJKnm01M9XySAtEFQAdBtTrW0at3mg5Kk6k9P6f+8WydJqm84ox01JyRJQ/JzY35tOKgcPxdUXtp1WN9a/44OsRwEXLAIKgC6zdYDx8PbkCXp1Q/qdeasX7NXbZIk9e/TSxMv9sb82uGDgkHlwLFmScFwc89TO/THnXX67u/f7eHKAaQqggqAbrNl/6eSpDEFeZKk9z5u0FsHjqvhdHBr8ndnj1GvrNi/di5t+5oDx5rla/XrjY+ORn3f480tMb8OQGYjqADoNu+0Le/cMW24st0ufdrcoj9/GAwcN0ws0oKy4XG/tsibq/zcbLUGLO092qSt+4+HPxewpI3/70iP1g4gNRFUAHQLf8DS+4caJElTRwwMd0iefbtWkjS2MC/h17tcLk0cGlwW2n7wM71fF/xek9oe+68dH/dI3QBSG0EFQLfY90mTTrX41ScnS6OG9AvPooSu8XNZUX6n32PaJYMkSRv+dkT7PgnOqiy/aZx6Zbm0Zf9xfdAWXgBcOFIiqKxevVojRoxQbm6uysrK9NZbbzldEoAuerf2hCRpQrFXWe5z3ZGQsUWJOyqSdN34QknS/91zTP6ApQF9emnysAG6blzw8T/urOveogGkPMeDyjPPPKPy8nItX75cO3bsUGlpqWbPnq2jR492/sUAHPfXvcf03scnwtuPS0uCASVyd8/Avjm6uO0KyYlcWpCnKcMHhO9PGT5ALpdLcyYGg8ovN+3Xj//0N+3/pKkb/wQAUpnjQWXlypW6++67tXjxYo0bN06PP/64+vTpo9/85jdOlwYggmVZ2l1/Uh/WN4avcPyH7R9rwa+26uaf/1VPv1UjSSobGVy+ieygzBw7RC6Xy9brLJ4+IvzxtWOGSJKmf+4iudu+/Fd/OaDbn9gSvsghgMyW7eSLt7S0aPv27aqoqAg/5na7NWvWLG3evLnD830+n3w+X/h+Y2Njj9T1/qEG/WG7ucG9ZC9rn9xXSUm+nKykX/F8XtPs6yX7ikn/+Qz/b5Hs67X4A3q39oSq206NHTqgtwryc/Xexyeinte7V5amXjJQkuTJztJ/3fN5vbTrsL72xUtsv9YNE4t09EaffK0B/fPUYZKkAX1zdNtVw8Jh6EijT//8xFZNGuqV1fbncrmkLJcrHGhCjwcsK/yxZVnB/8qS2+VSltslt8sll0tyKTjQa1mW/JalgBW8HIA/ELzvD1j68PBJNbe0qrSkv/rlZKtXtkvZbrdsZjAgLU0o9upLU4Y69vqOBpVjx47J7/eroKAg6vGCggJ9+OGHHZ5fWVmp+++/v8fr2n+sWVVvVvf46wDpJifbLbcreD2e0DV5rh0zWJ5st9746BNVXD9W+bm9ws+fMnxA1FKOHS6XS1++emSHxx+YO17zJ1+sUy1+faVqm3YdatCuQ84M13I9IlxIbi4tvnCDSldVVFSovLw8fL+xsVElJSXd/jqjh/TTvTNGJfW1yf7LKul/kCX5gsm+3vn8y9GV5Kuafk+Tfj3D/6xO/n3p+he6XdLwQX119ejgEszWA8flO+vXoH4eTRk2QG53z//Ze2W5ddWIYLfmpW9do017jqnx9NlwRyTUPQm0tY1ckuQK/mmjuybnOietgWDnRG1dl4AV7LSc67ZIbrdLWW33C/Jz5e3dS7vrT6rFH9DZthuQycYWdr5jryc5GlQuuugiZWVl6ciR6IOcjhw5osLCwg7P93g88nhiXyK+O11WlG9rKyVwoZrRNjvilNEFeRpd0Pkuop7yhUsHO/bawIXG0WHanJwcTZkyRRs3bgw/FggEtHHjRk2bNs3BygAAQCpwfOmnvLxcCxcu1JVXXqmpU6dq1apVam5u1uLFi50uDQAAOMzxoHLbbbfpk08+0bJly1RfX6/LL79cL7/8cocBWwAAcOFxWcnujU0BjY2N8nq9amhoUH4+MyUAAKSDrvz97fiBbwAAAPEQVAAAQMoiqAAAgJRFUAEAACmLoAIAAFIWQQUAAKQsggoAAEhZBBUAAJCyCCoAACBlOX6E/vkIHarb2NjocCUAAMCu0N/bdg7HT+ugcvLkSUlSSUmJw5UAAICuOnnypLxeb8LnpPW1fgKBgOrq6pSXlyeXy+V0OT2usbFRJSUlqq2t5dpGPYj32QzeZzN4n83gfe4ay7J08uRJFRcXy+1OPIWS1h0Vt9utoUOHOl2Gcfn5+fwfwQDeZzN4n83gfTaD99m+zjopIQzTAgCAlEVQAQAAKYugkkY8Ho+WL18uj8fjdCkZjffZDN5nM3ifzeB97jlpPUwLAAAyGx0VAACQsggqAAAgZRFUAABAyiKoAACAlEVQSRMPPvigPv/5z6tPnz7q379/zOfU1NTohhtuUJ8+fTRkyBB997vfVWtrq9lCM8yIESPkcrmibitWrHC6rLS3evVqjRgxQrm5uSorK9Nbb73ldEkZ54c//GGHn92xY8c6XVba27Rpk2666SYVFxfL5XLphRdeiPq8ZVlatmyZioqK1Lt3b82aNUt79uxxptgMQVBJEy0tLbr11lt1zz33xPy83+/XDTfcoJaWFr355pt68sknVVVVpWXLlhmuNPM88MADOnz4cPj2jW98w+mS0tozzzyj8vJyLV++XDt27FBpaalmz56to0ePOl1axhk/fnzUz+5f/vIXp0tKe83NzSotLdXq1atjfv4nP/mJfvrTn+rxxx/X1q1b1bdvX82ePVtnzpwxXGkGsZBW1q5da3m93g6Pv/jii5bb7bbq6+vDj61Zs8bKz8+3fD6fwQozy/Dhw63/+I//cLqMjDJ16lRryZIl4ft+v98qLi62KisrHawq8yxfvtwqLS11uoyMJsl6/vnnw/cDgYBVWFhoPfLII+HHTpw4YXk8Huvpp592oMLMQEclQ2zevFkTJ05UQUFB+LHZs2ersbFRH3zwgYOVpb8VK1Zo0KBBuuKKK/TII4+wnHYeWlpatH37ds2aNSv8mNvt1qxZs7R582YHK8tMe/bsUXFxsS655BItWLBANTU1TpeU0Q4cOKD6+vqon2+v16uysjJ+vs9DWl+UEOfU19dHhRRJ4fv19fVOlJQRvvnNb2ry5MkaOHCg3nzzTVVUVOjw4cNauXKl06WlpWPHjsnv98f8Wf3www8dqiozlZWVqaqqSmPGjNHhw4d1//3365prrtH777+vvLw8p8vLSKHftbF+vvk9nDw6Kg76/ve/32HYrf2NX97dryvve3l5ua699lpNmjRJ//Iv/6JHH31UP/vZz+Tz+Rz+UwCJzZkzR7feeqsmTZqk2bNn68UXX9SJEyf07LPPOl0a0CV0VBz0ne98R4sWLUr4nEsuucTW9yosLOywc+LIkSPhz+Gc83nfy8rK1Nraqurqao0ZM6YHqstsF110kbKyssI/myFHjhzh57SH9e/fX5deeqn27t3rdCkZK/QzfOTIERUVFYUfP3LkiC6//HKHqkp/BBUHDR48WIMHD+6W7zVt2jQ9+OCDOnr0qIYMGSJJ2rBhg/Lz8zVu3LhueY1McT7v+86dO+V2u8PvMbomJydHU6ZM0caNGzVv3jxJUiAQ0MaNG3Xvvfc6W1yGa2pq0r59+3TnnXc6XUrGGjlypAoLC7Vx48ZwMGlsbNTWrVvj7thE5wgqaaKmpkbHjx9XTU2N/H6/du7cKUkaNWqU+vXrp+uuu07jxo3TnXfeqZ/85Ceqr6/Xv//7v2vJkiVczTNJmzdv1tatWzVjxgzl5eVp8+bN+va3v6077rhDAwYMcLq8tFVeXq6FCxfqyiuv1NSpU7Vq1So1Nzdr8eLFTpeWUZYuXaqbbrpJw4cPV11dnZYvX66srCzdfvvtTpeW1pqamqK6UgcOHNDOnTs1cOBADRs2TPfdd59+/OMfa/To0Ro5cqR+8IMfqLi4OBzMkQSntx3BnoULF1qSOtxee+218HOqq6utOXPmWL1797Yuuugi6zvf+Y519uxZ54pOc9u3b7fKysosr9dr5ebmWpdddpn10EMPWWfOnHG6tLT3s5/9zBo2bJiVk5NjTZ061dqyZYvTJWWc2267zSoqKrJycnKsiy++2LrtttusvXv3Ol1W2nvttddi/i5euHChZVnBLco/+MEPrIKCAsvj8VgzZ860du/e7WzRac5lWZblVEgCAABIhF0/AAAgZRFUAABAyiKoAACAlEVQAQAAKYugAgAAUhZBBQAApCyCCgAASFkEFQAAkLIIKgAAIGURVAAAQMoiqAAAgJRFUAEAACnr/wNXayfPCpX1HAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_SARIMA_fit.resid.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "881e896f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Temp\\ipykernel_12860\\1367177785.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_data['Predicted_SARIMA']=pred_Sarima\n"
     ]
    }
   ],
   "source": [
    "test_data['Predicted_SARIMA']=pred_Sarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bf0c3078",
   "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>sm</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Moisture First Difference</th>\n",
       "      <th>Moisture Second Difference</th>\n",
       "      <th>Moisture 8 Difference</th>\n",
       "      <th>Predicted_ARIMA</th>\n",
       "      <th>Predicted_SARIMA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15124</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>37.484285</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15125</th>\n",
       "      <td>469.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>37.564775</td>\n",
       "      <td>37.439513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15126</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>37.619121</td>\n",
       "      <td>37.428846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15127</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.564508</td>\n",
       "      <td>37.429333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15128</th>\n",
       "      <td>468.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>37.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.551099</td>\n",
       "      <td>37.429820</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20160</th>\n",
       "      <td>326.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.249469</td>\n",
       "      <td>1.551191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.281404</td>\n",
       "      <td>1.573990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20162</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.313339</td>\n",
       "      <td>1.596775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20163</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-123.345274</td>\n",
       "      <td>1.539726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20164</th>\n",
       "      <td>327.0</td>\n",
       "      <td>pm3</td>\n",
       "      <td>27.93</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-123.377209</td>\n",
       "      <td>1.562292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5041 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          sm variable  value  Moisture First Difference  \\\n",
       "15124  469.0      pm3  37.43                       0.00   \n",
       "15125  469.0      pm3  37.43                       0.00   \n",
       "15126  468.0      pm3  37.43                       0.00   \n",
       "15127  468.0      pm3  37.43                       0.00   \n",
       "15128  468.0      pm3  37.43                       0.00   \n",
       "...      ...      ...    ...                        ...   \n",
       "20160  326.0      pm3  27.38                       0.00   \n",
       "20161  327.0      pm3  27.38                       0.00   \n",
       "20162  327.0      pm3  27.38                       0.00   \n",
       "20163  327.0      pm3  27.38                       0.00   \n",
       "20164  327.0      pm3  27.93                       0.55   \n",
       "\n",
       "       Moisture Second Difference  Moisture 8 Difference  Predicted_ARIMA  \\\n",
       "15124                        0.00                  -0.56        37.484285   \n",
       "15125                        0.00                  -0.56        37.564775   \n",
       "15126                        0.00                  -0.56        37.619121   \n",
       "15127                        0.00                   0.00        37.564508   \n",
       "15128                        0.00                   0.00        37.551099   \n",
       "...                           ...                    ...              ...   \n",
       "20160                        0.55                  -0.55      -123.249469   \n",
       "20161                        0.00                  -0.55      -123.281404   \n",
       "20162                        0.00                  -0.55      -123.313339   \n",
       "20163                        0.00                  -0.55      -123.345274   \n",
       "20164                        0.55                   0.00      -123.377209   \n",
       "\n",
       "       Predicted_SARIMA  \n",
       "15124               NaN  \n",
       "15125         37.439513  \n",
       "15126         37.428846  \n",
       "15127         37.429333  \n",
       "15128         37.429820  \n",
       "...                 ...  \n",
       "20160          1.551191  \n",
       "20161          1.573990  \n",
       "20162          1.596775  \n",
       "20163          1.539726  \n",
       "20164          1.562292  \n",
       "\n",
       "[5041 rows x 8 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c4ed646d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data[['value','Predicted_SARIMA','Predicted_ARIMA']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "493565dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sarth\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "model2 = SARIMAX(train_data['value'],order=(1,0,3),seasonal_order=(1,1,1,8))\n",
    "result2 = model2.fit()\n",
    "s.tail()\n",
    "future_pred=pd.date_range(start='2022-07-18',end='2023-03-31')\n",
    "pred2=result2.predict(start=len(s),end=len(s)+35,typ='levels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6c26aeb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20166   -28.859059\n",
       "20167   -28.922962\n",
       "20168   -28.919394\n",
       "20169   -28.915826\n",
       "20170   -28.912258\n",
       "20171   -28.962501\n",
       "20172   -28.958179\n",
       "20173   -28.953522\n",
       "20174   -28.950942\n",
       "20175   -29.014840\n",
       "20176   -29.011267\n",
       "20177   -29.007694\n",
       "20178   -29.004122\n",
       "20179   -29.054359\n",
       "20180   -29.050032\n",
       "20181   -29.045371\n",
       "20182   -29.042785\n",
       "20183   -29.106679\n",
       "20184   -29.103101\n",
       "20185   -29.099523\n",
       "20186   -29.095946\n",
       "20187   -29.146178\n",
       "20188   -29.141847\n",
       "20189   -29.137180\n",
       "20190   -29.134590\n",
       "20191   -29.198479\n",
       "20192   -29.194896\n",
       "20193   -29.191313\n",
       "20194   -29.187731\n",
       "20195   -29.237959\n",
       "20196   -29.233622\n",
       "20197   -29.228951\n",
       "20198   -29.226356\n",
       "20199   -29.290240\n",
       "20200   -29.286652\n",
       "20201   -29.283065\n",
       "Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2.to_csv('pred.csv')\n",
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eb98166a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22afe170f70>,\n",
       " <matplotlib.lines.Line2D at 0x22afe170fd0>,\n",
       " <matplotlib.lines.Line2D at 0x22bc5e50040>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test_data['SARIMA_pred']=pred2\n",
    "plt.rcParams['figure.figsize'] = [8, 5]\n",
    "plt.title(\"Amount of soil moisture from June 2022 - march 2023\")\n",
    "plt.plot(test_data[['value','Predicted_SARIMA','Predicted_ARIMA']])"
   ]
  }
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