8 Techniques to Model Seasonality

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) <a href="https://otexts.com/fpp2/components.html" rel="noopener ugc nofollow" target="_blank">highlights long-term effects such as trends or business cycles</a>. 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. <h2>Analysis example</h2> We’ll use the same process we did in the <a href="https://medium.com/towards-data-science/3-types-of-seasonality-and-how-to-detect-them-4e03f548d167" rel="noopener">previous article</a> (see also reference [1]): <a href="https://medium.com/towards-data-science/8-techniques-to-model-seasonality-2f81d739710">Read More</a>