In product demand forecasting, is it valid to use winsorization to remove large outliers (spikes) in the data? I understand that the spikes may be due to holiday effects (e.g. people will buy more chocolate during Valentine's day). However, they seem to be skewing the data.
The approach that I intend to adopt is that by Neal Wagner in Intelligent techniques for forecasting multiple time series in real-world systems in which explainable spikes are first removed, then added back in after the modeling task. The paper states that spikes are "removed", but how they are removed is never expounded upon. In my case, I intend to use winsorization.