time_series_tuna.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas as pd\r\n",
    "import requests\r\n",
    "import io\r\n",
    "import numpy as np\r\n",
    "from datetime import datetime\r\n",
    "import matplotlib\r\n",
    "import statsmodels\r\n",
    "import statsmodels.api as sm\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import itertools\r\n",
    "\r\n",
    "plt.style.use('fivethirtyeight')\r\n",
    "matplotlib.rcParams['axes.labelsize'] = 14\r\n",
    "matplotlib.rcParams['xtick.labelsize'] = 12\r\n",
    "matplotlib.rcParams['ytick.labelsize'] = 12\r\n",
    "matplotlib.rcParams['text.color'] = 'k'\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "df = pd.read_csv('iotc_tuna.csv')\r\n",
    "df['Year'] = pd.to_datetime(df['Year'], format = '%d/%m/%Y')\r\n",
    "print(df.info())"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 2 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Year              71 non-null     datetime64[ns]\n",
      " 1   Catch/Capture(t)  71 non-null     float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 1.2 KB\n",
      "None\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# reformatting date\r\n",
    "\r\n",
    "date_input = '1950'\r\n",
    "datetimeobject = datetime.strptime(date_input, '%Y')\r\n",
    "\r\n",
    "new_format = datetimeobject.strftime('%Y-%m-%d')\r\n",
    "print(new_format)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "1950-01-01\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "#plotting time-series catch data using matplotlib\r\n",
    "catch = df.set_index('Year')\r\n",
    "catch.plot(figsize=(15, 6))\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "#decomposition plot: trend, seasonality and noise\r\n",
    "from pylab import rcParams\r\n",
    "rcParams['figure.figsize'] = 18, 8\r\n",
    "\r\n",
    "decomposition = sm.tsa.seasonal_decompose(catch, model='additive')\r\n",
    "fig = decomposition.plot()\r\n",
    "plt.show()\r\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x576 with 4 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "#ARIMA Time Series Model parameter selection\r\n",
    "p = d = q = range(0, 2)\r\n",
    "pdq = list(itertools.product(p, d, q))\r\n",
    "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]\r\n",
    "\r\n",
    "print('Examples of parameter combinations for Seasonal ARIMA...')\r\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))\r\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))\r\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))\r\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Examples of parameter combinations for Seasonal ARIMA...\n",
      "SARIMAX: (0, 0, 1) x (0, 0, 1, 12)\n",
      "SARIMAX: (0, 0, 1) x (0, 1, 0, 12)\n",
      "SARIMAX: (0, 1, 0) x (0, 1, 1, 12)\n",
      "SARIMAX: (0, 1, 0) x (1, 0, 0, 12)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "\r\n",
    "for param in pdq:\r\n",
    "    for param_seasonal in seasonal_pdq:\r\n",
    "        try:\r\n",
    "            mod = sm.tsa.statespace.SARIMAX(catch, order=param, seasonal_order=param_seasonal, enforce_stationarity=False, enforce_invertibility=False)\r\n",
    "            results = mod.fit()\r\n",
    "            print('ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))')\r\n",
    "        except:\r\n",
    "            continue"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\base\\model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n",
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ARIMA{}x{}2 - AIC:{}.format(param, param_seasonal, results.aic))\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "#Fitting the ARIMA model\r\n",
    "mod = sm.tsa.statespace.SARIMAX(catch,\r\n",
    "                                order=(1, 1, 1),\r\n",
    "                                seasonal_order=(1, 1, 0, 12),\r\n",
    "                                enforce_stationarity=False,\r\n",
    "                                enforce_invertibility=False)\r\n",
    "results = mod.fit()\r\n",
    "print(results.summary().tables[1])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.4918      0.892      0.551      0.581      -1.256       2.240\n",
      "ma.L1         -0.3078      0.895     -0.344      0.731      -2.061       1.446\n",
      "ar.S.L12      -0.6454      0.227     -2.844      0.004      -1.090      -0.201\n",
      "sigma2      6.341e+09   5.48e-10   1.16e+19      0.000    6.34e+09    6.34e+09\n",
      "==============================================================================\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "results.plot_diagnostics(figsize=(16, 8))\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "#Producing 1-step ahead forecast plot\r\n",
    "pred = results.get_prediction(start=pd.to_datetime('01/01/2000'))\r\n",
    "pred_ci = pred.conf_int()\r\n",
    "ax = catch['1950':].plot(label='observed catch')\r\n",
    "pred.predicted_mean.plot(ax=ax, label='One-Step Ahead Forecast', alpha=.7, figsize=(14, 7))\r\n",
    "ax.fill_between(pred_ci.index,\r\n",
    "                pred_ci.iloc[:, 0],\r\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.2)\r\n",
    "ax.set_xlabel('Year')\r\n",
    "ax.set_ylabel('Catch (Mt)')\r\n",
    "plt.legend()\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\jackr\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:132: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.\n",
      "  date_key = Timestamp(key, freq=base_index.freq)\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "#Mean Squared Error of forecast\r\n",
    "catch_forecasted = pred.predicted_mean\r\n",
    "catch_truth = catch['01/01/1990':]\r\n",
    "\r\n",
    "mse = ((catch_forecasted - catch_truth) ** 2).mean()\r\n",
    "print('The Mean Squared Error of our forecast is {}'.format(round(mse, 2)))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The Mean Squared Error of our forecast is 2000-01-01 00:00:00   NaN\n",
      "2001-01-01 00:00:00   NaN\n",
      "2002-01-01 00:00:00   NaN\n",
      "2003-01-01 00:00:00   NaN\n",
      "2004-01-01 00:00:00   NaN\n",
      "2005-01-01 00:00:00   NaN\n",
      "2006-01-01 00:00:00   NaN\n",
      "2007-01-01 00:00:00   NaN\n",
      "2008-01-01 00:00:00   NaN\n",
      "2009-01-01 00:00:00   NaN\n",
      "2010-01-01 00:00:00   NaN\n",
      "2011-01-01 00:00:00   NaN\n",
      "2012-01-01 00:00:00   NaN\n",
      "2013-01-01 00:00:00   NaN\n",
      "2014-01-01 00:00:00   NaN\n",
      "2015-01-01 00:00:00   NaN\n",
      "2016-01-01 00:00:00   NaN\n",
      "2017-01-01 00:00:00   NaN\n",
      "2018-01-01 00:00:00   NaN\n",
      "2019-01-01 00:00:00   NaN\n",
      "2020-01-01 00:00:00   NaN\n",
      "Catch/Capture(t)      NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "#Root mean squared error of forecast\r\n",
    "print('The Root Mean Squared Error of our forecasts is {}'.format(round(np.sqrt(mse), 2)))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The Root Mean Squared Error of our forecasts is 2000-01-01 00:00:00   NaN\n",
      "2001-01-01 00:00:00   NaN\n",
      "2002-01-01 00:00:00   NaN\n",
      "2003-01-01 00:00:00   NaN\n",
      "2004-01-01 00:00:00   NaN\n",
      "2005-01-01 00:00:00   NaN\n",
      "2006-01-01 00:00:00   NaN\n",
      "2007-01-01 00:00:00   NaN\n",
      "2008-01-01 00:00:00   NaN\n",
      "2009-01-01 00:00:00   NaN\n",
      "2010-01-01 00:00:00   NaN\n",
      "2011-01-01 00:00:00   NaN\n",
      "2012-01-01 00:00:00   NaN\n",
      "2013-01-01 00:00:00   NaN\n",
      "2014-01-01 00:00:00   NaN\n",
      "2015-01-01 00:00:00   NaN\n",
      "2016-01-01 00:00:00   NaN\n",
      "2017-01-01 00:00:00   NaN\n",
      "2018-01-01 00:00:00   NaN\n",
      "2019-01-01 00:00:00   NaN\n",
      "2020-01-01 00:00:00   NaN\n",
      "Catch/Capture(t)      NaN\n",
      "dtype: float64\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "pred_uc = results.get_forecast(steps=10)\r\n",
    "pred_ci = pred_uc.conf_int()\r\n",
    "ax = catch.plot(label='observed', figsize=(14, 7))\r\n",
    "pred_uc.predicted_mean.plot(ax=ax, label='Forecast')\r\n",
    "ax.fill_between(pred_ci.index,\r\n",
    "                pred_ci.iloc[:, 0],\r\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.25)\r\n",
    "ax.set_xlabel('Year')\r\n",
    "ax.set_ylabel('Catch (Mt)')\r\n",
    "plt.legend()\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.9.7",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.7 64-bit (windows store)"
  },
  "interpreter": {
   "hash": "fc65848199ab5d3afbf5110e7a5e0cec1968acdf0dd6acfe3c6bd85b656f1267"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}