The Seasonal ARIMA (SARIMA) model, as its name implies, is a designated version of the ARIMA model for time series with a seasonal component. As we saw in Chapter 6, Seasonality Analysis, and Chapter 7, Correlation Analysis, a time series with a seasonal component has a strong relationship with its seasonal lags. The SARIMA model is utilizing the seasonal lags in a similar manner to how the ARIMA model is utilizing the non-seasonal lags with the AR and MA processes and differencing. It does this by adding the following three components to the ARIMA model:
- SAR(P) process: A seasonal AR process of the series with its past P seasonal lags. For example, a SAR(2) is an AR process of the series with its past two seasonal lags, that is,
, where Φ represents the seasonal coefficient of the SAR process, and f represents the series frequency. - SMA(Q) process...
