Statsmodels example: Autoregressive Moving Average (ARMA) Model

ex_arma2.ipynb

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  "name": "ex_arma2"
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Autoregressive Moving Average (ARMA) Model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import statsmodels.api as sm\n",
      "from statsmodels.tsa.arima_process import arma_generate_sample\n",
      "np.random.seed(12345)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate some data from an ARMA process:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arparams = np.array([.75, -.25])\n",
      "maparams = np.array([.65, .35])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "arparams = np.r_[1, -arparams]\n",
      "maparam = np.r_[1, maparams]\n",
      "nobs = 250\n",
      "y = arma_generate_sample(arparams, maparams, nobs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "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",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n",
      "y = pandas.TimeSeries(y, index=dates)\n",
      "arma_mod = sm.tsa.ARMA(y, freq='M')\n",
      "arma_res = arma_mod.fit(order=(2,2), trend='nc', disp=-1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/local/lib/python2.7/dist-packages/statsmodels-0.5.0-py2.7-linux-x86_64.egg/statsmodels/tsa/arima_model.py:226: FutureWarning: In the next release order will not be optional in the model constructor.\n",
        "  \"in the model constructor.\", FutureWarning)\n",
        "/usr/local/lib/python2.7/dist-packages/statsmodels-0.5.0-py2.7-linux-x86_64.egg/statsmodels/tsa/arima_model.py:657: FutureWarning: The order argument to fit is deprecated. Please use the model constructor argument order. This will overwrite any order given in the model constructor.\n",
        "  \"constructor.\", FutureWarning)\n"
       ]
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}