ISF2021: How to Make Multiplicative ETS Work for You

This year International Symposium on Forecasting was held online, although Centre for Marketing Analytics and Forecasting of Lancaster University had their own hub, where we would come and watch presentations together and even present to the others.

Presentation at ISF2021 Lancaster Hub

Presentation at ISF2021 Lancaster Hub

I presented on the topic of Multiplicative ETS, based on this chapter of the ADAM textbook and on the paper John Boylan and I are working on. In this presentation, I discuss that the point forecasts from the ETS(M,*,*) models in general do not correspond to conditional expectations (and not even to geometric means) and show the difference between the two. Furthermore, we propose using Log Normal, Gamma and Inverse Gaussian distributions for the error term of the model instead of the Normal one. This makes the model more realistic and help in forecasting on low volume data. All of this is already implemented in adam() function of smooth package for R.

Here are the slides.

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