There are several ways of handling seasonality. Some approaches remove the seasonal component before modeling. Seasonally-adjusted data (a time series minus the seasonal component) highlights long-term effects such as trends or business cycles. Other approaches add extra variables that capture the cyclical nature of seasonality.
Before going over different methods, let’s create a time series and describe its seasonal patterns.
Analysis example
We’ll use the same process we did in the previous article (see also reference [1]):