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Hey @Manuel, How are you evaluating the predictions? Or is this a qualitative evaluation? On the side of controlling the seasonality using a multiplicative approach. It should be possible to create a Box-Cox log scaler in the TemporalNorm module. Using a logarithmic transformation (multiplicative deleveling) could reduce the predictions variance. Another idea that has given me good results is to add a

SeasonalNaive

feature to better control the seasonality in the

futr_exog_list

parameter. Let me know your thoughts.

@Kin Gtz. Olivares Thanks! Previously I had tried to log transform the data, perform the forecast on the transformed data and then reverse the log transform with exp, but it did not seem to work well (I was getting overly smoothed forecasts). About the last idea, would you use a naive seasonal model to perform a forecast and then use that forecast as an exogenous variable for NHITS?

Yes, use Seasonal Naive as a model input. To help it better model the seasonalities. Its almost like modeling the I residuals vs SeasonalNaive, in an AR I MA model.