Autoregressive integrated moving-average (ARIMA) models predict values of a dependent time series with a linear combination of its own past values, past errors (also called shocks or innovations), and current and past values of other time series (predictor time series).
The Time Series Forecasting System uses the ARIMA procedure of SAS/ETS software to fit and forecast ARIMA models. The maximum likelihood method is used for parameter estimation. Refer to Chapter 8: The ARIMA Procedure, for details of ARIMA model estimation and forecasting.
This section summarizes the notation used for ARIMA models.