Autoregressive (AR) Models:
The autoregressive (AR) models are used in time series analysis . to describe stationary time series . These models represent time series that are generated by passing the white noise through a recursive linear filter . The output of such a filter at the moment 
is a weighted sum of 
previous values of the filter output. The integer parameter is called the order of the AR-model.
The AR-model of a random process 
in discrete time is defined by the following expression:
 |
where
-
are the coefficients of the recursive filter; -
is the order of the model;
-
are output uncorrelated errors.
See also: moving average models , autoregressive and moving average models (ARMA) , ARIMA .