lab09/TS-Exp9,10-ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4a9ce27",
   "metadata": {},
   "source": [
    "# Ex 9: Implement the ARIMA forecasting technique in the given data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12dc39ad-d896-4f0b-afb4-48ae2e4e33fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from datetime import datetime\n",
    "from itertools import product\n",
    "from tqdm import tqdm_notebook\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07760410-65ae-4281-bc18-f96c28458971",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parser(x):\n",
    "    \"\"\"\n",
    "    Parses a string in the format 'yyyy-mm' and returns a datetime object.\n",
    "\n",
    "    Args:\n",
    "        x (str): The string to be parsed in the format 'yyyy-mm'.\n",
    "\n",
    "    Returns:\n",
    "        datetime: A datetime object representing the parsed date.\n",
    "\n",
    "    Example:\n",
    "        >>> parser('21-01')\n",
    "        datetime.datetime(2021, 1, 1, 0, 0)\n",
    "    \"\"\"\n",
    "    return datetime.strptime('190'+x, '%Y-%m')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89e8a72e",
   "metadata": {},
   "source": [
    "# Importing the Dataset and Exploring it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32abdbad-38f1-4ec0-b466-8ca3155dffa3",
   "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>Sunspots</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1749-01-01</th>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-02-01</th>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-03-01</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-04-01</th>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-05-01</th>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Sunspots\n",
       "Month               \n",
       "1749-01-01      58.0\n",
       "1749-02-01      62.6\n",
       "1749-03-01      70.0\n",
       "1749-04-01      55.7\n",
       "1749-05-01      85.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = pd.read_csv('https://raw.githubusercontent.com/jbrownlee/Datasets/master/monthly-sunspots.csv', header=0, parse_dates=[0], index_col=0)\n",
    "series.head() # Viewing the first 5 entires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c8d1765-1bb5-409b-9440-c43844953003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series.plot() # Plotting a normal line plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "78a1bd3e-b8fa-4ca4-86d3-16796755a709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "autocorrelation_plot(series) # Plotting the autocorrelation plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d95734a-1085-4e34-b3a1-fb074f0c28ab",
   "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>Sunspots</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1749-01</th>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-02</th>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-03</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-04</th>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749-05</th>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sunspots\n",
       "Month            \n",
       "1749-01      58.0\n",
       "1749-02      62.6\n",
       "1749-03      70.0\n",
       "1749-04      55.7\n",
       "1749-05      85.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.index = series.index.to_period('M') # Preprocesses the given time series data by converting the index to monthly periods\n",
    "series.head() # View first 5 updated entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c23ca61b-5159-4da9-9cb4-33f2f11683a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:               Sunspots   No. Observations:                 2820\n",
      "Model:                 ARIMA(5, 1, 0)   Log Likelihood              -11780.322\n",
      "Date:                Mon, 25 Mar 2024   AIC                          23572.644\n",
      "Time:                        13:45:22   BIC                          23608.309\n",
      "Sample:                    01-31-1749   HQIC                         23585.513\n",
      "                         - 12-31-1983                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.4085      0.014    -28.182      0.000      -0.437      -0.380\n",
      "ar.L2         -0.2891      0.016    -18.195      0.000      -0.320      -0.258\n",
      "ar.L3         -0.1923      0.016    -12.137      0.000      -0.223      -0.161\n",
      "ar.L4         -0.0961      0.016     -6.129      0.000      -0.127      -0.065\n",
      "ar.L5         -0.0531      0.015     -3.659      0.000      -0.082      -0.025\n",
      "sigma2       249.6956      4.131     60.439      0.000     241.598     257.793\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):              1370.26\n",
      "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.39   Skew:                             0.43\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.30\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# ARIMA model\n",
    "model = ARIMA(series, order=(5,1,0)) # Define model\n",
    "model_fit = model.fit() # Fit the model\n",
    "print(model_fit.summary()) # Summarize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c0f9c64-e051-41cd-808f-ef1ad4cfdd15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a Residual plot\n",
    "residuals = pd.DataFrame(model_fit.resid) # Creating a new residuals DataFrame\n",
    "residuals.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aea0326d-6965-483f-8676-835e09042847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a Density plot for residuals\n",
    "residuals.plot(kind='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28aa8dd6-72ea-4ad1-87e1-9445143f2c1c",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2820.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.007876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.836376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-71.629565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-8.114013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.544398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.766544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>94.983859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "count  2820.000000\n",
       "mean      0.007876\n",
       "std      15.836376\n",
       "min     -71.629565\n",
       "25%      -8.114013\n",
       "50%      -0.544398\n",
       "75%       7.766544\n",
       "max      94.983859"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals.describe() # Viewing the summary statistics of residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eff6bd1-491e-4069-a81a-af6acbd8d14b",
   "metadata": {},
   "source": [
    "# Rolling Forecast ARIMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bb3ac8bc-4d05-4520-99d4-a496b721d3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the values from the series\n",
    "X = series.values\n",
    "\n",
    "# Calculate the index at which to split the dataset into training and testing sets\n",
    "# Here, 80% of the data is used for training and the rest for testing\n",
    "size = int(len(X) * 0.8)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "# The training set includes elements from the start to the calculated index\n",
    "# The testing set includes elements from the calculated index to the end of the dataset\n",
    "train, test = X[0:size], X[size:len(X)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0062ed43-ad74-4ec0-b870-8c48d546c972",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:107.93195823477072, expected:[132.5]\n",
      "Predicted:119.73631195976171, expected:[128.5]\n",
      "Predicted:121.84571455153184, expected:[83.9]\n",
      "Predicted:99.5211797611051, expected:[109.3]\n",
      "Predicted:110.87115317361798, expected:[116.7]\n",
      "Predicted:115.45350272144394, expected:[130.3]\n",
      "Predicted:122.16832271103817, expected:[145.1]\n",
      "Predicted:133.22376564723342, expected:[137.7]\n",
      "Predicted:130.76093832300805, expected:[100.7]\n",
      "Predicted:113.44256131615334, expected:[124.9]\n",
      "Predicted:124.36919409080777, expected:[74.4]\n",
      "Predicted:97.02312452273226, expected:[88.8]\n",
      "Predicted:97.34608358433306, expected:[98.4]\n",
      "Predicted:100.8846930474231, expected:[119.2]\n",
      "Predicted:108.43798905035167, expected:[86.5]\n",
      "Predicted:94.55959445152148, expected:[101.]\n",
      "Predicted:97.57427512891276, expected:[127.4]\n",
      "Predicted:115.57490153913531, expected:[97.5]\n",
      "Predicted:101.7101933232893, expected:[165.3]\n",
      "Predicted:139.51723404100466, expected:[115.7]\n",
      "Predicted:119.54939918484573, expected:[89.6]\n",
      "Predicted:102.80173120601138, expected:[99.1]\n",
      "Predicted:107.50314099270489, expected:[122.2]\n",
      "Predicted:115.89400240267229, expected:[92.7]\n",
      "Predicted:103.73460072061053, expected:[80.3]\n",
      "Predicted:90.42656333471172, expected:[77.4]\n",
      "Predicted:85.3711247701559, expected:[64.6]\n",
      "Predicted:75.58159904760852, expected:[109.1]\n",
      "Predicted:97.86026348595598, expected:[118.3]\n",
      "Predicted:104.98991408011626, expected:[101.]\n",
      "Predicted:97.77118639802102, expected:[97.6]\n",
      "Predicted:97.74364295070654, expected:[105.8]\n",
      "Predicted:102.47451114276859, expected:[112.6]\n",
      "Predicted:109.32456069459342, expected:[88.1]\n",
      "Predicted:96.70033111039972, expected:[68.1]\n",
      "Predicted:81.99564055034739, expected:[42.1]\n",
      "Predicted:63.33547092053786, expected:[50.5]\n",
      "Predicted:61.48162189813564, expected:[59.4]\n",
      "Predicted:62.997485438536856, expected:[83.3]\n",
      "Predicted:73.16664923958352, expected:[60.7]\n",
      "Predicted:62.337953173206515, expected:[54.4]\n",
      "Predicted:56.90601034587638, expected:[83.9]\n",
      "Predicted:74.36548436778918, expected:[67.5]\n",
      "Predicted:68.10114732922807, expected:[105.5]\n",
      "Predicted:89.84730198202377, expected:[66.5]\n",
      "Predicted:72.61098604389545, expected:[55.]\n",
      "Predicted:63.18732165818825, expected:[58.4]\n",
      "Predicted:65.49019273899799, expected:[68.3]\n",
      "Predicted:67.53278082236253, expected:[45.6]\n",
      "Predicted:56.102195359235715, expected:[44.5]\n",
      "Predicted:50.0369923818605, expected:[46.4]\n",
      "Predicted:49.442483138550436, expected:[32.8]\n",
      "Predicted:40.42986294250503, expected:[29.5]\n",
      "Predicted:36.393886092779645, expected:[59.8]\n",
      "Predicted:50.43645883918871, expected:[66.9]\n",
      "Predicted:56.970209567687114, expected:[60.]\n",
      "Predicted:55.62571743345636, expected:[65.9]\n",
      "Predicted:60.396517901203715, expected:[46.3]\n",
      "Predicted:51.41572381226886, expected:[38.3]\n",
      "Predicted:46.662394843106895, expected:[33.7]\n",
      "Predicted:42.19683740374445, expected:[35.6]\n",
      "Predicted:39.868181368893545, expected:[52.8]\n",
      "Predicted:48.25900519638327, expected:[54.2]\n",
      "Predicted:49.20841448613328, expected:[60.7]\n",
      "Predicted:53.79673662673633, expected:[25.]\n",
      "Predicted:35.963529516045966, expected:[11.4]\n",
      "Predicted:25.042983361219463, expected:[17.7]\n",
      "Predicted:26.017116611043765, expected:[20.2]\n",
      "Predicted:24.201929021384384, expected:[17.2]\n",
      "Predicted:20.79909389781568, expected:[19.2]\n",
      "Predicted:18.939832359835748, expected:[30.7]\n",
      "Predicted:24.99509880788715, expected:[22.5]\n",
      "Predicted:22.353073015809816, expected:[12.4]\n",
      "Predicted:16.664900160163242, expected:[28.9]\n",
      "Predicted:25.002455666797857, expected:[27.4]\n",
      "Predicted:25.473771072249793, expected:[26.1]\n",
      "Predicted:25.362908608944334, expected:[14.1]\n",
      "Predicted:18.739959339223752, expected:[7.6]\n",
      "Predicted:13.203019703923218, expected:[13.2]\n",
      "Predicted:15.644512206515838, expected:[19.4]\n",
      "Predicted:18.071990874919706, expected:[10.]\n",
      "Predicted:12.704923493676691, expected:[7.8]\n",
      "Predicted:9.997827725087355, expected:[10.2]\n",
      "Predicted:10.694299162687832, expected:[18.8]\n",
      "Predicted:15.540277868268596, expected:[3.7]\n",
      "Predicted:8.12266695113908, expected:[0.5]\n",
      "Predicted:4.375215569051827, expected:[11.]\n",
      "Predicted:9.498672252210945, expected:[0.3]\n",
      "Predicted:3.7036196488031967, expected:[2.5]\n",
      "Predicted:3.9893668766512844, expected:[5.]\n",
      "Predicted:4.5621683002822575, expected:[5.]\n",
      "Predicted:4.288057298753949, expected:[16.7]\n",
      "Predicted:11.633167324820786, expected:[14.3]\n",
      "Predicted:11.359159171612838, expected:[16.9]\n",
      "Predicted:13.726073304531798, expected:[10.8]\n",
      "Predicted:11.739241804908056, expected:[28.4]\n",
      "Predicted:21.441346764447303, expected:[18.5]\n",
      "Predicted:18.726426685597925, expected:[12.7]\n",
      "Predicted:14.850797351750748, expected:[21.5]\n",
      "Predicted:19.895937141484353, expected:[32.]\n",
      "Predicted:25.978010082709094, expected:[30.6]\n",
      "Predicted:27.596497313756107, expected:[36.2]\n",
      "Predicted:31.243791758892037, expected:[42.6]\n",
      "Predicted:36.50316735907101, expected:[25.9]\n",
      "Predicted:29.34433299607904, expected:[34.9]\n",
      "Predicted:34.01044167889178, expected:[68.8]\n",
      "Predicted:53.898659003351014, expected:[46.]\n",
      "Predicted:45.287634480418625, expected:[27.4]\n",
      "Predicted:35.02452880493408, expected:[47.6]\n",
      "Predicted:44.608184229482774, expected:[86.2]\n",
      "Predicted:67.69652632264241, expected:[76.6]\n",
      "Predicted:68.60087185821132, expected:[75.7]\n",
      "Predicted:69.45334900849303, expected:[84.9]\n",
      "Predicted:77.14826788396307, expected:[73.5]\n",
      "Predicted:74.0625849493496, expected:[116.2]\n",
      "Predicted:100.00148981886306, expected:[107.2]\n",
      "Predicted:99.709467306081, expected:[94.4]\n",
      "Predicted:93.99610968651956, expected:[102.3]\n",
      "Predicted:100.4184886683084, expected:[123.8]\n",
      "Predicted:112.88894475720534, expected:[121.7]\n",
      "Predicted:116.58886222646527, expected:[115.7]\n",
      "Predicted:114.23298573177583, expected:[113.4]\n",
      "Predicted:113.53928202561775, expected:[129.8]\n",
      "Predicted:123.43437796424223, expected:[149.8]\n",
      "Predicted:137.63124719802812, expected:[201.3]\n",
      "Predicted:170.51754694496145, expected:[163.9]\n",
      "Predicted:158.44258380498385, expected:[157.9]\n",
      "Predicted:156.93796551206452, expected:[188.8]\n",
      "Predicted:178.0702041936641, expected:[169.4]\n",
      "Predicted:170.4801058393141, expected:[163.6]\n",
      "Predicted:168.61061833282872, expected:[128.]\n",
      "Predicted:145.9709706297259, expected:[116.5]\n",
      "Predicted:133.33031106331248, expected:[108.5]\n",
      "Predicted:124.7967726516342, expected:[86.1]\n",
      "Predicted:104.61074130382184, expected:[94.8]\n",
      "Predicted:102.88665527574008, expected:[189.7]\n",
      "Predicted:153.5155725482504, expected:[174.]\n",
      "Predicted:153.89367024729222, expected:[167.8]\n",
      "Predicted:155.56168365865668, expected:[142.2]\n",
      "Predicted:146.3523998200742, expected:[157.9]\n",
      "Predicted:155.1151782111247, expected:[143.3]\n",
      "Predicted:152.1631904297285, expected:[136.3]\n",
      "Predicted:143.56806146239654, expected:[95.8]\n",
      "Predicted:117.94029537922289, expected:[138.]\n",
      "Predicted:134.1160931378247, expected:[119.1]\n",
      "Predicted:125.20482896059175, expected:[182.3]\n",
      "Predicted:157.1407395704612, expected:[157.5]\n",
      "Predicted:151.9526721843082, expected:[147.]\n",
      "Predicted:143.94081931465166, expected:[106.2]\n",
      "Predicted:126.06940288779823, expected:[121.7]\n",
      "Predicted:127.27991428890397, expected:[125.8]\n",
      "Predicted:131.7342066407668, expected:[123.8]\n",
      "Predicted:125.58197193505288, expected:[145.3]\n",
      "Predicted:137.30132894231662, expected:[131.6]\n",
      "Predicted:129.88680500372755, expected:[143.5]\n",
      "Predicted:137.64056671629513, expected:[117.6]\n",
      "Predicted:125.74220447504729, expected:[101.6]\n",
      "Predicted:113.15896749932986, expected:[94.8]\n",
      "Predicted:107.65497333536135, expected:[109.7]\n",
      "Predicted:111.03277170905754, expected:[113.4]\n",
      "Predicted:112.95345866222796, expected:[106.2]\n",
      "Predicted:106.92523889676762, expected:[83.6]\n",
      "Predicted:93.14740942141214, expected:[91.]\n",
      "Predicted:94.42298614618963, expected:[85.2]\n",
      "Predicted:91.03740677185206, expected:[51.3]\n",
      "Predicted:68.7518827388759, expected:[61.4]\n",
      "Predicted:69.08172743820947, expected:[54.8]\n",
      "Predicted:62.148392453468816, expected:[54.1]\n",
      "Predicted:58.430016063551975, expected:[59.9]\n",
      "Predicted:60.536878507101406, expected:[59.9]\n",
      "Predicted:58.37547582787185, expected:[59.9]\n",
      "Predicted:59.22531677766392, expected:[92.9]\n",
      "Predicted:78.47162111925013, expected:[108.5]\n",
      "Predicted:92.11934926654085, expected:[100.6]\n",
      "Predicted:92.31476579193345, expected:[61.5]\n",
      "Predicted:72.85698677930282, expected:[61.]\n",
      "Predicted:69.79355717295815, expected:[83.1]\n",
      "Predicted:82.32496896516983, expected:[51.6]\n",
      "Predicted:63.58860344072839, expected:[52.4]\n",
      "Predicted:59.36067803249417, expected:[45.8]\n",
      "Predicted:52.67165909320065, expected:[40.7]\n",
      "Predicted:46.44732517861041, expected:[22.7]\n",
      "Predicted:35.34893569660038, expected:[22.]\n",
      "Predicted:29.19182981454195, expected:[29.1]\n",
      "Predicted:31.283911367478606, expected:[23.4]\n",
      "Predicted:26.282444248993265, expected:[36.4]\n",
      "Predicted:32.52822773697365, expected:[39.3]\n",
      "Predicted:34.87521028487194, expected:[54.9]\n",
      "Predicted:44.9227505280716, expected:[28.2]\n",
      "Predicted:33.19720581953857, expected:[23.8]\n",
      "Predicted:28.57220260021195, expected:[22.1]\n",
      "Predicted:27.838576880502224, expected:[34.3]\n",
      "Predicted:32.48789118746831, expected:[26.5]\n",
      "Predicted:29.134687539665862, expected:[3.9]\n",
      "Predicted:13.386210533216445, expected:[10.]\n",
      "Predicted:14.35568285564904, expected:[27.8]\n",
      "Predicted:23.508545218530386, expected:[12.5]\n",
      "Predicted:15.542749069987286, expected:[21.8]\n",
      "Predicted:19.481809356862975, expected:[8.6]\n",
      "Predicted:12.290730244269213, expected:[23.5]\n",
      "Predicted:19.45077231077308, expected:[19.3]\n",
      "Predicted:19.741208384832333, expected:[8.2]\n",
      "Predicted:11.579671057422193, expected:[1.6]\n",
      "Predicted:7.733956454037411, expected:[2.5]\n",
      "Predicted:5.765656398129622, expected:[0.2]\n",
      "Predicted:3.8590506481266966, expected:[0.5]\n",
      "Predicted:2.3867366993463395, expected:[10.9]\n",
      "Predicted:7.381489524497462, expected:[1.8]\n",
      "Predicted:2.7373856389801583, expected:[0.8]\n",
      "Predicted:1.7072694564725195, expected:[0.2]\n",
      "Predicted:1.5175139543103735, expected:[4.8]\n",
      "Predicted:3.5170298892105003, expected:[8.4]\n",
      "Predicted:6.506498671587019, expected:[1.5]\n",
      "Predicted:2.487912232105211, expected:[7.]\n",
      "Predicted:5.442772727153561, expected:[9.2]\n",
      "Predicted:7.465595891954082, expected:[7.6]\n",
      "Predicted:6.952394657705608, expected:[23.1]\n",
      "Predicted:16.546306094319533, expected:[20.8]\n",
      "Predicted:17.032460183597387, expected:[4.9]\n",
      "Predicted:8.835683847045694, expected:[11.3]\n",
      "Predicted:12.071602167737336, expected:[28.9]\n",
      "Predicted:22.297834312283626, expected:[31.7]\n",
      "Predicted:26.063663936458376, expected:[26.7]\n",
      "Predicted:24.6469382090021, expected:[40.7]\n",
      "Predicted:33.301414161863704, expected:[42.7]\n",
      "Predicted:37.34776028932954, expected:[58.5]\n",
      "Predicted:48.72985323214485, expected:[89.2]\n",
      "Predicted:70.45509331433423, expected:[76.9]\n",
      "Predicted:68.69320223696853, expected:[73.6]\n",
      "Predicted:70.05960812998633, expected:[124.]\n",
      "Predicted:102.35885285201795, expected:[118.4]\n",
      "Predicted:106.33098514095403, expected:[110.7]\n",
      "Predicted:106.14773315509788, expected:[136.6]\n",
      "Predicted:124.27428608318993, expected:[116.6]\n",
      "Predicted:116.18097597512964, expected:[129.1]\n",
      "Predicted:125.42858368322642, expected:[169.6]\n",
      "Predicted:151.3840922895489, expected:[173.2]\n",
      "Predicted:158.12013312708154, expected:[155.3]\n",
      "Predicted:153.43335469001852, expected:[201.3]\n",
      "Predicted:181.75790060404037, expected:[192.1]\n",
      "Predicted:183.46189523968638, expected:[165.]\n",
      "Predicted:170.9915497773927, expected:[130.2]\n",
      "Predicted:150.4376492703934, expected:[157.4]\n",
      "Predicted:159.21071870098925, expected:[175.2]\n",
      "Predicted:170.78169098347882, expected:[164.6]\n",
      "Predicted:163.78209963031378, expected:[200.7]\n",
      "Predicted:185.05700592107897, expected:[187.2]\n",
      "Predicted:180.94401264509406, expected:[158.]\n",
      "Predicted:166.17268724660642, expected:[235.8]\n",
      "Predicted:211.82000794928797, expected:[253.8]\n",
      "Predicted:229.09185586015707, expected:[210.9]\n",
      "Predicted:211.62577465553815, expected:[239.4]\n",
      "Predicted:230.63348960652243, expected:[202.5]\n",
      "Predicted:210.48327845530278, expected:[164.9]\n",
      "Predicted:188.32723341836015, expected:[190.7]\n",
      "Predicted:198.46732071185852, expected:[196.]\n",
      "Predicted:195.6425381748741, expected:[175.3]\n",
      "Predicted:184.08634970414252, expected:[171.5]\n",
      "Predicted:178.1051048368294, expected:[191.4]\n",
      "Predicted:185.8475722014165, expected:[200.2]\n",
      "Predicted:193.44545507066684, expected:[201.2]\n",
      "Predicted:196.3939654209268, expected:[181.5]\n",
      "Predicted:185.70468528043676, expected:[152.3]\n",
      "Predicted:167.11698914994346, expected:[187.6]\n",
      "Predicted:184.69083459993502, expected:[217.4]\n",
      "Predicted:202.94414898350303, expected:[143.1]\n",
      "Predicted:162.4706347598656, expected:[185.7]\n",
      "Predicted:182.4364168657115, expected:[163.3]\n",
      "Predicted:168.95885845016363, expected:[172.]\n",
      "Predicted:172.13240499090855, expected:[168.7]\n",
      "Predicted:173.12185506632906, expected:[149.6]\n",
      "Predicted:155.95595392942457, expected:[199.6]\n",
      "Predicted:185.7593985250513, expected:[145.2]\n",
      "Predicted:156.87723485886977, expected:[111.4]\n",
      "Predicted:133.7574521973982, expected:[124.]\n",
      "Predicted:135.25000284887005, expected:[125.]\n",
      "Predicted:129.80185551391176, expected:[146.3]\n",
      "Predicted:142.22098782873897, expected:[106.]\n",
      "Predicted:117.2311779050429, expected:[102.2]\n",
      "Predicted:110.3648169052417, expected:[122.]\n",
      "Predicted:120.45407500260038, expected:[119.6]\n",
      "Predicted:118.43933787288485, expected:[110.2]\n",
      "Predicted:113.97219246966038, expected:[121.7]\n",
      "Predicted:118.31016890531887, expected:[134.1]\n",
      "Predicted:126.40496416489995, expected:[127.2]\n",
      "Predicted:125.35455654602913, expected:[82.8]\n",
      "Predicted:100.15603358460265, expected:[89.6]\n",
      "Predicted:99.02359538159409, expected:[85.6]\n",
      "Predicted:93.83347696158856, expected:[57.9]\n",
      "Predicted:74.19240885769922, expected:[46.1]\n",
      "Predicted:61.866193780077666, expected:[53.]\n",
      "Predicted:58.80697862545613, expected:[61.4]\n",
      "Predicted:61.26305170584127, expected:[51.]\n",
      "Predicted:54.47613100158513, expected:[77.4]\n",
      "Predicted:68.03822492386742, expected:[70.2]\n",
      "Predicted:66.24657899686099, expected:[55.9]\n",
      "Predicted:59.314021411925474, expected:[63.6]\n",
      "Predicted:63.942219595851164, expected:[37.7]\n",
      "Predicted:47.84898963728218, expected:[32.6]\n",
      "Predicted:42.59335400321332, expected:[40.]\n",
      "Predicted:43.49597505098131, expected:[38.7]\n",
      "Predicted:40.197290367895704, expected:[50.3]\n",
      "Predicted:46.72451203031159, expected:[45.6]\n",
      "Predicted:44.01875994781528, expected:[46.4]\n",
      "Predicted:44.87415826145591, expected:[43.7]\n",
      "Predicted:44.371433124305085, expected:[42.]\n",
      "Predicted:43.04352958834095, expected:[21.8]\n",
      "Predicted:31.296424246429993, expected:[21.8]\n",
      "Predicted:28.138425763045902, expected:[51.3]\n",
      "Predicted:43.407134206359295, expected:[39.5]\n",
      "Predicted:38.01484432183763, expected:[26.9]\n",
      "Predicted:31.17972384193017, expected:[23.2]\n",
      "Predicted:27.587751259545403, expected:[19.8]\n",
      "Predicted:23.93151825587745, expected:[24.4]\n",
      "Predicted:26.252532305998823, expected:[17.1]\n",
      "Predicted:20.603109129251525, expected:[29.3]\n",
      "Predicted:26.119540236303784, expected:[43.]\n",
      "Predicted:35.0210545316694, expected:[35.9]\n",
      "Predicted:32.98897332431097, expected:[19.6]\n",
      "Predicted:24.943231639048257, expected:[33.2]\n",
      "Predicted:31.489958457772563, expected:[38.8]\n",
      "Predicted:35.51210666190947, expected:[35.3]\n",
      "Predicted:34.6514650251118, expected:[23.4]\n",
      "Predicted:27.895902280807178, expected:[14.9]\n",
      "Predicted:21.024744312499088, expected:[15.3]\n",
      "Predicted:19.842750027345907, expected:[17.7]\n",
      "Predicted:19.655621394423974, expected:[16.5]\n",
      "Predicted:17.857314506845583, expected:[8.6]\n",
      "Predicted:12.228620067265785, expected:[9.5]\n",
      "Predicted:11.36764634861881, expected:[9.1]\n",
      "Predicted:10.47144238680008, expected:[3.1]\n",
      "Predicted:6.373915442884204, expected:[9.3]\n",
      "Predicted:8.989614891321414, expected:[4.7]\n",
      "Predicted:5.916099552980429, expected:[6.1]\n",
      "Predicted:6.308752477778208, expected:[7.4]\n",
      "Predicted:7.098489338025541, expected:[15.1]\n",
      "Predicted:11.376588918320776, expected:[17.5]\n",
      "Predicted:14.209895020613516, expected:[14.2]\n",
      "Predicted:13.173545903052936, expected:[11.7]\n",
      "Predicted:12.353262597036112, expected:[6.8]\n",
      "Predicted:9.408736346695514, expected:[24.1]\n",
      "Predicted:19.093232802898612, expected:[15.9]\n",
      "Predicted:15.665334612953188, expected:[11.9]\n",
      "Predicted:13.268998963973209, expected:[8.9]\n",
      "Predicted:11.403073410158349, expected:[16.8]\n",
      "Predicted:14.908613982254012, expected:[20.1]\n",
      "Predicted:17.97638129002798, expected:[15.8]\n",
      "Predicted:15.665402684226734, expected:[17.]\n",
      "Predicted:16.51561676635792, expected:[28.2]\n",
      "Predicted:23.250803323332587, expected:[24.4]\n",
      "Predicted:22.718981013222354, expected:[25.3]\n",
      "Predicted:24.054457229900233, expected:[48.7]\n",
      "Predicted:38.39065708701796, expected:[45.3]\n",
      "Predicted:39.44109699090527, expected:[47.7]\n",
      "Predicted:43.42113576007962, expected:[56.7]\n",
      "Predicted:50.57337398753527, expected:[51.2]\n",
      "Predicted:49.24753959564841, expected:[50.2]\n",
      "Predicted:50.46766049371751, expected:[57.2]\n",
      "Predicted:54.61203653299583, expected:[57.2]\n",
      "Predicted:55.351388566768506, expected:[70.4]\n",
      "Predicted:64.14338444703841, expected:[110.9]\n",
      "Predicted:89.99227811311255, expected:[93.6]\n",
      "Predicted:86.10620062870663, expected:[111.8]\n",
      "Predicted:100.46502942128757, expected:[69.5]\n",
      "Predicted:79.90580310410428, expected:[86.5]\n",
      "Predicted:87.33417399954287, expected:[67.3]\n",
      "Predicted:77.610124057687, expected:[91.5]\n",
      "Predicted:86.88450243840644, expected:[107.2]\n",
      "Predicted:98.57882995856649, expected:[76.8]\n",
      "Predicted:81.02282952549032, expected:[88.2]\n",
      "Predicted:88.00177829651086, expected:[94.3]\n",
      "Predicted:91.16960178903506, expected:[126.4]\n",
      "Predicted:111.4409814347608, expected:[121.8]\n",
      "Predicted:114.12919936604797, expected:[111.9]\n",
      "Predicted:109.8317589569495, expected:[92.2]\n",
      "Predicted:100.30918144372168, expected:[81.2]\n",
      "Predicted:91.62676168026435, expected:[127.2]\n",
      "Predicted:116.62842957321195, expected:[110.3]\n",
      "Predicted:108.78553845015651, expected:[96.1]\n",
      "Predicted:100.27199511621116, expected:[109.3]\n",
      "Predicted:107.19381215799015, expected:[117.2]\n",
      "Predicted:111.76828277719234, expected:[107.7]\n",
      "Predicted:109.28019347425263, expected:[86.]\n",
      "Predicted:95.62981734603683, expected:[109.8]\n",
      "Predicted:106.51061837472744, expected:[104.4]\n",
      "Predicted:104.38981141384814, expected:[120.5]\n",
      "Predicted:113.6685106646743, expected:[135.8]\n",
      "Predicted:124.87951223044644, expected:[106.8]\n",
      "Predicted:110.27091075703702, expected:[120.]\n",
      "Predicted:118.69342157674492, expected:[106.]\n",
      "Predicted:110.94907738318543, expected:[96.8]\n",
      "Predicted:104.10373774219188, expected:[98.]\n",
      "Predicted:103.26381110261913, expected:[91.3]\n",
      "Predicted:96.07826477422992, expected:[95.7]\n",
      "Predicted:97.38876391223724, expected:[93.5]\n",
      "Predicted:94.85494202827488, expected:[97.9]\n",
      "Predicted:96.5018022573239, expected:[111.5]\n",
      "Predicted:105.04890739960246, expected:[127.8]\n",
      "Predicted:116.35641933746919, expected:[102.9]\n",
      "Predicted:105.50347579273367, expected:[109.5]\n",
      "Predicted:109.17286931478247, expected:[127.5]\n",
      "Predicted:120.4850632911042, expected:[106.8]\n",
      "Predicted:110.39190276585319, expected:[112.5]\n",
      "Predicted:113.54859978564149, expected:[93.]\n",
      "Predicted:101.05488664589964, expected:[99.5]\n",
      "Predicted:102.3696166463143, expected:[86.6]\n",
      "Predicted:94.41201852621197, expected:[95.2]\n",
      "Predicted:95.77230866150047, expected:[83.5]\n",
      "Predicted:88.8138192866984, expected:[91.3]\n",
      "Predicted:90.72869193186028, expected:[79.]\n",
      "Predicted:83.95214127910785, expected:[60.7]\n",
      "Predicted:70.89324425230238, expected:[71.8]\n",
      "Predicted:74.74324759397908, expected:[57.5]\n",
      "Predicted:64.4216327543008, expected:[49.8]\n",
      "Predicted:57.518443181176984, expected:[81.]\n",
      "Predicted:73.11209916625263, expected:[61.4]\n",
      "Predicted:62.74409269025914, expected:[50.2]\n",
      "Predicted:56.1694690475966, expected:[51.7]\n",
      "Predicted:55.298192580605956, expected:[63.2]\n",
      "Predicted:60.26152537118088, expected:[82.2]\n",
      "Predicted:73.1517742439132, expected:[61.5]\n",
      "Predicted:62.87383138592344, expected:[88.4]\n",
      "Predicted:78.44612597432562, expected:[80.1]\n",
      "Predicted:77.10654664406142, expected:[63.2]\n",
      "Predicted:68.39642508403125, expected:[80.5]\n",
      "Predicted:78.31139052896552, expected:[88.]\n",
      "Predicted:82.36713802324384, expected:[76.5]\n",
      "Predicted:78.02390202791834, expected:[76.8]\n",
      "Predicted:77.82993397150938, expected:[64.]\n",
      "Predicted:69.5338526088384, expected:[61.3]\n",
      "Predicted:66.742444636242, expected:[41.6]\n",
      "Predicted:53.51866694055171, expected:[45.3]\n",
      "Predicted:51.20024158518743, expected:[43.4]\n",
      "Predicted:47.854781578055466, expected:[42.9]\n",
      "Predicted:45.09910541183346, expected:[46.]\n",
      "Predicted:46.035893429857815, expected:[57.7]\n",
      "Predicted:52.086148171995134, expected:[42.4]\n",
      "Predicted:44.90831972585023, expected:[39.5]\n",
      "Predicted:42.5848619700085, expected:[23.1]\n",
      "Predicted:32.146334786368016, expected:[25.6]\n",
      "Predicted:30.639323681180112, expected:[59.3]\n",
      "Predicted:49.08641792598179, expected:[30.7]\n",
      "Predicted:34.150152339192275, expected:[23.9]\n",
      "Predicted:29.285501577704615, expected:[23.3]\n",
      "Predicted:27.309088594535293, expected:[27.6]\n",
      "Predicted:28.17927731504572, expected:[26.]\n",
      "Predicted:27.92894063063283, expected:[21.3]\n",
      "Predicted:23.34093145898689, expected:[40.3]\n",
      "Predicted:33.75779249349342, expected:[39.5]\n",
      "Predicted:35.17126379554193, expected:[36.]\n",
      "Predicted:34.66516387014061, expected:[55.8]\n",
      "Predicted:47.197391493221524, expected:[33.6]\n",
      "Predicted:36.64170334324104, expected:[40.2]\n",
      "Predicted:40.5433042699755, expected:[47.1]\n",
      "Predicted:44.73505854925892, expected:[25.]\n",
      "Predicted:31.9451654869112, expected:[20.5]\n",
      "Predicted:28.046366830405205, expected:[18.9]\n",
      "Predicted:23.882889910051567, expected:[11.5]\n",
      "Predicted:17.66153031337798, expected:[11.5]\n",
      "Predicted:15.679745914644524, expected:[5.1]\n",
      "Predicted:9.524566858954387, expected:[9.]\n",
      "Predicted:10.072016000512926, expected:[11.4]\n",
      "Predicted:10.928002511492263, expected:[28.2]\n",
      "Predicted:20.55256940339605, expected:[39.7]\n",
      "Predicted:29.735928388932773, expected:[13.9]\n",
      "Predicted:17.5727815275399, expected:[9.1]\n",
      "Predicted:14.459079631640606, expected:[19.4]\n",
      "Predicted:19.211393291890694, expected:[7.8]\n",
      "Predicted:12.429963219754011, expected:[8.1]\n",
      "Predicted:11.399839470346265, expected:[4.3]\n",
      "Predicted:7.170073106733005, expected:[21.9]\n",
      "Predicted:16.287098112039587, expected:[18.8]\n",
      "Predicted:16.395130581698247, expected:[12.4]\n",
      "Predicted:12.995658633160822, expected:[12.2]\n",
      "Predicted:13.13769569241104, expected:[1.9]\n",
      "Predicted:6.640686069829109, expected:[16.4]\n",
      "Predicted:14.278762815650001, expected:[13.5]\n",
      "Predicted:12.853480033569799, expected:[20.6]\n",
      "Predicted:16.873160337211708, expected:[5.2]\n",
      "Predicted:9.157936656889001, expected:[15.3]\n",
      "Predicted:13.680232311941902, expected:[16.4]\n",
      "Predicted:15.38557655195708, expected:[23.1]\n",
      "Predicted:19.29108554281305, expected:[8.7]\n",
      "Predicted:12.368834419400368, expected:[12.9]\n",
      "Predicted:13.34030197356364, expected:[18.6]\n",
      "Predicted:17.010820447720487, expected:[38.5]\n",
      "Predicted:28.983842952731642, expected:[21.4]\n",
      "Predicted:22.069811147945828, expected:[30.1]\n",
      "Predicted:26.87797780065288, expected:[44.]\n",
      "Predicted:36.66965657505865, expected:[43.8]\n",
      "Predicted:38.82269134400562, expected:[29.1]\n",
      "Predicted:32.72266732269847, expected:[43.2]\n",
      "Predicted:39.76219643661311, expected:[51.9]\n",
      "Predicted:46.237592254397164, expected:[93.6]\n",
      "Predicted:72.89786968532133, expected:[76.5]\n",
      "Predicted:69.41792012204205, expected:[99.7]\n",
      "Predicted:85.65753018753198, expected:[82.7]\n",
      "Predicted:81.64355775329736, expected:[95.1]\n",
      "Predicted:89.79262157780184, expected:[70.4]\n",
      "Predicted:78.90997234862091, expected:[58.1]\n",
      "Predicted:68.1106507214674, expected:[138.2]\n",
      "Predicted:113.15736492134742, expected:[125.1]\n",
      "Predicted:111.62315173686062, expected:[97.9]\n",
      "Predicted:100.49705339591833, expected:[122.7]\n",
      "Predicted:115.53343162443201, expected:[166.6]\n",
      "Predicted:143.20234275925205, expected:[137.5]\n",
      "Predicted:135.5161239131197, expected:[138.]\n",
      "Predicted:137.36722403216095, expected:[101.5]\n",
      "Predicted:116.10045195462031, expected:[134.4]\n",
      "Predicted:131.57602989198887, expected:[149.5]\n",
      "Predicted:142.1962893506488, expected:[159.4]\n",
      "Predicted:148.29070044649356, expected:[142.2]\n",
      "Predicted:142.49541008110697, expected:[188.4]\n",
      "Predicted:169.27947733294312, expected:[186.2]\n",
      "Predicted:175.28990767596483, expected:[183.3]\n",
      "Predicted:177.5991122329908, expected:[176.3]\n",
      "Predicted:177.13704727050418, expected:[159.6]\n",
      "Predicted:166.56774835743678, expected:[155.]\n",
      "Predicted:163.337857670134, expected:[126.2]\n",
      "Predicted:143.11505940270126, expected:[164.1]\n",
      "Predicted:159.63171367909356, expected:[179.9]\n",
      "Predicted:169.35066155011484, expected:[157.3]\n",
      "Predicted:157.9208217263295, expected:[136.3]\n",
      "Predicted:146.52805447134003, expected:[135.4]\n",
      "Predicted:142.3872594261302, expected:[155.]\n",
      "Predicted:152.41107243031468, expected:[164.7]\n",
      "Predicted:158.50942901203268, expected:[147.9]\n",
      "Predicted:149.53565068639, expected:[174.4]\n",
      "Predicted:164.83272327251376, expected:[114.]\n",
      "Predicted:132.2647637951488, expected:[141.3]\n",
      "Predicted:143.50857086716405, expected:[135.5]\n",
      "Predicted:139.8667307766525, expected:[156.4]\n",
      "Predicted:148.50969139344602, expected:[127.5]\n",
      "Predicted:135.4047479518091, expected:[90.]\n",
      "Predicted:108.7834193313474, expected:[143.8]\n",
      "Predicted:136.28544968880007, expected:[158.7]\n",
      "Predicted:145.70306610466136, expected:[167.3]\n",
      "Predicted:154.26159898541238, expected:[162.4]\n",
      "Predicted:155.99313381951936, expected:[137.5]\n",
      "Predicted:143.21466144764523, expected:[150.1]\n",
      "Predicted:151.45983209504104, expected:[111.2]\n",
      "Predicted:128.3153669889469, expected:[163.6]\n",
      "Predicted:153.4454211063387, expected:[153.8]\n",
      "Predicted:150.3124345551431, expected:[122.]\n",
      "Predicted:131.05180608523497, expected:[82.2]\n",
      "Predicted:106.69805182107945, expected:[110.4]\n",
      "Predicted:114.67269512277616, expected:[106.1]\n",
      "Predicted:110.9663207946076, expected:[107.6]\n",
      "Predicted:108.24555191497694, expected:[118.8]\n",
      "Predicted:114.03002952738288, expected:[94.7]\n",
      "Predicted:100.00814588710938, expected:[98.1]\n",
      "Predicted:101.59608507503654, expected:[127.]\n",
      "Predicted:117.61595520629504, expected:[84.3]\n",
      "Predicted:94.6071654594273, expected:[51.]\n",
      "Predicted:72.52239307778584, expected:[66.5]\n",
      "Predicted:75.10747645474561, expected:[80.7]\n",
      "Predicted:79.30418861456899, expected:[99.2]\n",
      "Predicted:89.95243210260385, expected:[91.1]\n",
      "Predicted:86.55471240458031, expected:[82.2]\n",
      "Predicted:82.4236644832275, expected:[71.8]\n",
      "Predicted:77.67924161766493, expected:[50.3]\n",
      "Predicted:63.657576256748676, expected:[55.8]\n",
      "Predicted:63.128621401761066, expected:[33.3]\n",
      "Predicted:46.54184995921083, expected:[33.4]\n"
     ]
    }
   ],
   "source": [
    "# Initialize the history list with the training data\n",
    "history = [x for x in train]\n",
    "\n",
    "# Initialize an empty list for storing predictions\n",
    "predictions = list()\n",
    "\n",
    "# Loop over each element in the test set\n",
    "for t in range(len(test)):\n",
    "    # Create an ARIMA model with the current history and order (5,1,0)\n",
    "    model = ARIMA(history, order=(5,1,0))\n",
    "    \n",
    "    # Fit the model to the data\n",
    "    model_fit = model.fit()\n",
    "    \n",
    "    # Forecast the next value\n",
    "    yhat = model_fit.forecast()[0]\n",
    "    \n",
    "    # Append the forecasted value to the predictions list\n",
    "    predictions.append(yhat)\n",
    "    \n",
    "    # Add the actual observed value to the history for the next iteration\n",
    "    history.append(test[t])\n",
    "    \n",
    "    # Print the forecasted and the actual value\n",
    "    print(f'Predicted:{yhat}, expected:{test[t]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "65336aab-74dc-41de-bb0e-8aa5872c5da7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE:  18.909569874425742\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the forecast using RMSE\n",
    "rmse = np.sqrt(sklearn.metrics.mean_squared_error(test, predictions))\n",
    "print(\"Test RMSE: \", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d84e9fc-b005-4ac7-94b1-cfd72b91dc9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test) # Plotting the updated dataset\n",
    "plt.plot(predictions, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f283a01e-cef7-41c0-b88d-0a71376408f4",
   "metadata": {},
   "source": [
    "# Ex 10: Implement the Seasonal ARIMA forecasting technique in the given data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "897fdc98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install ipywidgets\n",
    "# !jupyter nbextension enable --py widgetsnbextension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80db32f9-8234-4ac9-8933-dde34a0b2f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the range of values for the AR parameter (p) for the ARIMA model\n",
    "ps = range(0, 5)\n",
    "\n",
    "# Set the value for the differencing component (d) of the ARIMA model\n",
    "d = 1\n",
    "\n",
    "# Set the range of values for the MA parameter (q) for the ARIMA model\n",
    "qs = range(0, 5)\n",
    "\n",
    "# Set the range of values for the seasonal AR parameter (P) for the ARIMA model\n",
    "Ps = range(0, 5)\n",
    "\n",
    "# Set the value for the seasonal differencing component (D) of the ARIMA model\n",
    "D = 1\n",
    "\n",
    "# Set the range of values for the seasonal MA parameter (Q) for the ARIMA model\n",
    "Qs = range(0, 5)\n",
    "\n",
    "# Set the length of the seasonal period (s) for the ARIMA model\n",
    "s = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f73cd77-aacf-480d-a5b6-6711bc19cc7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "625\n"
     ]
    }
   ],
   "source": [
    "# Create a list with all possible combinations of parameters\n",
    "\n",
    "# Use the product function from itertools module to generate all possible combinations of parameters\n",
    "parameters = product(ps, qs, Ps, Qs)\n",
    "\n",
    "# Convert the combinations to a list\n",
    "parameters_list = list(parameters)\n",
    "\n",
    "# Print the number of combinations\n",
    "print(len(parameters_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "def80e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_SARIMA(parameters_list, d, D, s):\n",
    "    \"\"\"\n",
    "    This function optimizes the SARIMA model parameters to find the best model with the lowest AIC value.\n",
    "    \n",
    "    parameters_list - list with (p, q, P, Q) tuples representing the parameters for the AR, MA, seasonal AR, \n",
    "                      and seasonal MA parts of the SARIMA model.\n",
    "    d - integration order, the number of nonseasonal differences needed for stationarity.\n",
    "    D - seasonal integration order, the number of seasonal differences needed for stationarity.\n",
    "    s - length of season, the period of the seasonality.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Initialize an empty list to store the results\n",
    "    results = []\n",
    "    \n",
    "    # Initialize the best AIC to infinity\n",
    "    best_aic = float('inf')\n",
    "    \n",
    "    # Loop over each parameter combination in the list\n",
    "    for param in tqdm_notebook(parameters_list):\n",
    "        try: \n",
    "            # Create and fit a SARIMA model with the current parameter combination\n",
    "            model = sm.tsa.statespace.SARIMAX(test, \n",
    "                                              order=(param[0], d, param[1]), \n",
    "                                              seasonal_order=(param[2], D, param[3], s)).fit(disp=-1)\n",
    "        except:\n",
    "            # If the model fails to fit, skip to the next iteration\n",
    "            continue\n",
    "            \n",
    "        # Get the AIC of the model\n",
    "        aic = model.aic\n",
    "        \n",
    "        # If the current model's AIC is less than the best AIC found so far,\n",
    "        # update the best AIC, best model, and best parameters\n",
    "        if aic < best_aic:\n",
    "            best_model = model\n",
    "            best_aic = aic\n",
    "            best_param = param\n",
    "        \n",
    "        # Append the current parameter combination and its corresponding AIC to the results list\n",
    "        results.append([param, model.aic])\n",
    "    \n",
    "    # Convert the results list to a DataFrame\n",
    "    result_table = pd.DataFrame(results)\n",
    "    \n",
    "    # Set the column names of the DataFrame\n",
    "    result_table.columns = ['parameters', 'aic']\n",
    "    \n",
    "    # Sort the DataFrame by the AIC in ascending order and reset the index\n",
    "    result_table = result_table.sort_values(by='aic', ascending=True).reset_index(drop=True)\n",
    "    \n",
    "    # Return the result table\n",
    "    return result_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84f44cd2-8c7b-4426-8589-0594639c5c76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-702f3420df87>:19: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for param in tqdm_notebook(parameters_list):\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ef86dd0debc42969d1f3fd64f4f27d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/625 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\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\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\Admin\\AppData\\Roaming\\Python\\Python38\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "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>parameters</th>\n",
       "      <th>aic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(1, 4, 2, 3)</td>\n",
       "      <td>4868.344199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(1, 3, 4, 2)</td>\n",
       "      <td>4868.801472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(1, 4, 1, 4)</td>\n",
       "      <td>4869.275645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(1, 3, 3, 1)</td>\n",
       "      <td>4869.341861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(1, 3, 3, 2)</td>\n",
       "      <td>4869.537106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     parameters          aic\n",
       "0  (1, 4, 2, 3)  4868.344199\n",
       "1  (1, 3, 4, 2)  4868.801472\n",
       "2  (1, 4, 1, 4)  4869.275645\n",
       "3  (1, 3, 3, 1)  4869.341861\n",
       "4  (1, 3, 3, 2)  4869.537106"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_table = optimize_SARIMA(parameters_list, d, D, s)\n",
    "result_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0cf180c2-29c1-45c8-b56c-fe3e101b51cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: 1 4 2 3\n"
     ]
    }
   ],
   "source": [
    "# Set parameters that give the lowest AIC (Akaike Information Criteria)\n",
    "p, q, P, Q = result_table.parameters[0]\n",
    "print(\"Best parameters:\", p, q, P, Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9652ec68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a dataframe containing actual and predicted prices\n",
    "comparison = pd.DataFrame({'actual': [18.93, 19.23, 19.08, 19.17, 19.11, 19.12],\n",
    "'predicted': [18.96, 18.97, 18.96, 18.92, 18.94, 18.92]},\n",
    "index = pd.date_range(start='2018-06-05', periods=6,))\n",
    "#Plot predicted vs actual price\n",
    "plt.figure(figsize=(17, 8))\n",
    "plt.plot(comparison.actual)\n",
    "plt.plot(comparison.predicted)\n",
    "plt.title('Predicted closing price of New Germany Fund Inc (GF)')\n",
    "plt.ylabel('Closing price ($)')\n",
    "plt.xlabel('Trading day')\n",
    "# plt.legend(loc='best')\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}