Criteria for Classifying Forecasting Methods
Abstract
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers. We argue that this distinction does not stem from fundamental differences in the methods assigned to either class. Instead, this distinction is probably of a tribal nature, which limits the insights into the appropriateness and effectiveness of different forecasting methods. We provide alternative characteristics of forecasting methods which, in our view, allow to draw meaningful conclusions. Further, we discuss areas of forecasting which could benefit most from cross-pollination between the ML and the statistics communities.
Cite
@article{arxiv.2212.03523,
title = {Criteria for Classifying Forecasting Methods},
author = {Tim Januschowski and Jan Gasthaus and Yuyang Wang and David Salinas and Valentin Flunkert and Michael Bohlke-Schneider and Laurent Callot},
journal= {arXiv preprint arXiv:2212.03523},
year = {2022}
}