| Abstract: | This discussion reflects on the results of the M4 forecasting competition, and in particular, the impact of machine learning (ML) methods. Unlike the M3, which included only one ML method (an automatic artificial neural network that performed poorly), M4’s 49 participants included eight that used either pure ML approaches, or ML in conjunction with statistical methods. The six pure (or combination of pure) ML methods again fared poorly, with all of them falling below the Comb benchmark that combined three simple time series methods. However, utilizing ML either in combination with statistical methods (and for selecting weightings) or in a hybrid model with exponential smoothing not only exceeded the benchmark, but performed at the top. While these promising results by no means prove ML to be a panacea, they do challenge the notion that complex methods do not add value to the forecasting process. |
