Many economic and business variables are affected by seasonal factors. For example, power usage is highest in the months when temperatures are most extreme. The most common type of seasonality is variation due to the time of year, but other types of seasonality are also found in time series data.
Seasonal models are often multiplicative rather than additive.
A multiplicative model includes the product of one or more nonseasonal parameters with one or more seasonal parameters. For multiplicative MA processes, there are small spikes in the ACF plot q lags before and after the seasonal lag, where q is the number of nonseasonal MA parameters necessary to model the data. These small spikes are usually in the opposite direction of the seasonal spike. An additive MA process typically has small spikes q lags before the seasonal lag, where q is the number of nonseasonal MA parameters necessary to model the data. To identify an AR process, look for the patterns described previously in the IACF plot rather than in the ACF plot.