{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoregressive Moving Average (ARMA): Artificial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "from statsmodels.tsa.arima_process import arma_generate_sample\n",
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some data from an ARMA process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "arparams = np.array([.75, -.25])\n",
    "maparams = np.array([.65, .35])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "arparams = np.r_[1, -arparams]\n",
    "maparams = np.r_[1, maparams]\n",
    "nobs = 250\n",
    "y = arma_generate_sample(arparams, maparams, nobs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now, optionally, we can add some dates information. For this example, we'll use a pandas time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/tsa/base/tsa_model.py:159: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    }
   ],
   "source": [
    "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n",
    "y = pd.Series(y, index=dates)\n",
    "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n",
    "arma_res = arma_mod.fit(trend='nc', disp=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                  250\n",
      "Model:                     ARMA(2, 2)   Log Likelihood                -353.445\n",
      "Method:                       css-mle   S.D. of innovations              0.990\n",
      "Date:                Fri, 24 Apr 2020   AIC                            716.891\n",
      "Time:                        14:17:04   BIC                            734.498\n",
      "Sample:                    01-31-1980   HQIC                           723.977\n",
      "                         - 10-31-2000                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1.y        0.7904      0.134      5.878      0.000       0.527       1.054\n",
      "ar.L2.y       -0.2314      0.113     -2.044      0.041      -0.453      -0.009\n",
      "ma.L1.y        0.7007      0.127      5.525      0.000       0.452       0.949\n",
      "ma.L2.y        0.4061      0.095      4.291      0.000       0.221       0.592\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            1.7079           -1.1851j            2.0788           -0.0965\n",
      "AR.2            1.7079           +1.1851j            2.0788            0.0965\n",
      "MA.1           -0.8628           -1.3108j            1.5693           -0.3427\n",
      "MA.2           -0.8628           +1.3108j            1.5693            0.3427\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "print(arma_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-06-30    0.173211\n",
       "2000-07-31   -0.048325\n",
       "2000-08-31   -0.415804\n",
       "2000-09-30    0.338725\n",
       "2000-10-31    0.360838\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "fig = arma_res.plot_predict(start='1999-06-30', end='2001-05-31', ax=ax)\n",
    "legend = ax.legend(loc='upper left')"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
