“statistical and machine learning forecasting methods： concerns and ways forward”

Roy Mendelssohn points us to this paper by Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos, which begins:

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined.

and continues:

Moreover, we observed that their computational requirements are considerably greater than those of statistical methods.

Mendelssohn writes:

For time series, ML models don’t work as well as traditional methods, at least to date. I have read a little on some of the methods. Some have layers of NNs. The residuals from one layer are passed to the next. I would hate to guess what the “equivalent number of parameters” would be (yes I know these are non-parametric but there has to be a lot of over-fitting going on).

I haven’t looked much at these particular models, but for the general problem of “equivalent number of parameters,” let me point you to this paper and this paper with Aki et al.