Autoregressive Moving Average (ARMA) Model
==========================================


.. ipython:: python

   
   import numpy as np
   import statsmodels.api as sm
   

Generate some data from an ARMA process

.. ipython:: python

   from statsmodels.tsa.arima_process import arma_generate_sample
   
   np.random.seed(12345)
   arparams = np.array([.75, -.25])
   maparams = np.array([.65, .35])
   

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.

.. ipython:: python

   arparams = np.r_[1, -arparams]
   maparam = np.r_[1, maparams]
   nobs = 250
   y = arma_generate_sample(arparams, maparams, nobs)
   

Now, optionally, we can add some dates information. For this example,
we'll use a pandas time series.

.. ipython:: python

   import pandas
   dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
   y = pandas.TimeSeries(y, index=dates)
   arma_mod = sm.tsa.ARMA(y, freq='M')
   arma_res = arma_mod.fit(order=(2,2), trend='nc', disp=-1)
   