Statistical and Machine Learning forecasting methods: Concerns and ways forward.

ABSTRACT: 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. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses

SUBMITTER: Makridakis S 

PROVIDER: S-EPMC5870978 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature