Seasonality is a common characteristic of time series. It can appear in two forms: additive and multiplicative. In the former case the amplitude of the seasonal variation is independent of the level, whereas in the latter it is connected. The following figure highlights this:
Note that in the example of multiplicative seasonality the season is becoming “wider”. Obviously if the level was decreasing the seasonal amplitude of the multiplicative case would decrease as well. For selecting the appropriate model to produce our forecasts we need to know the type of seasonality we are dealing with. How do you compare against statistical identification? Select additive or multiplicative in the demonstration below and submit your choice to see if you can do better than statistics and the average accuracy of participants so far.