{
 "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:                Sun, 16 Aug 2020   AIC                            716.891\n",
      "Time:                        18:00:44   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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
