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Fathoming empirical forecasting competitions’ winners

Alroomi, Azzam; Karamatzanis, George; Nikolopoulos, Kostas; Tilba, Anna; Xiao, Shujun

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Authors

Azzam Alroomi



Abstract

The M5 forecasting competition has provided strong empirical evidence that machine learning methods can outperform statistical methods: in essence, complex methods can be more accurate than simple ones. This result, be as it may, challenges the flagship empirical result that led the forecasting discipline for the last four decades: keep methods sophisticatedly simple. Nevertheless, this was a first, and thus we could argue this may not happen again. There has been a different winner in each forecasting competition. This inevitably raises the question: can a method win more than once (and should it be expected to)? Furthermore, we argue for the need to elaborate on the perks of competing methods, and what makes them winners?

Citation

Alroomi, A., Karamatzanis, G., Nikolopoulos, K., Tilba, A., & Xiao, S. (2022). Fathoming empirical forecasting competitions’ winners. International Journal of Forecasting, 38(4), 1519-1525. https://doi.org/10.1016/j.ijforecast.2022.03.010

Journal Article Type Article
Acceptance Date Mar 31, 2022
Online Publication Date Oct 5, 2022
Publication Date 2022-12
Deposit Date Apr 22, 2022
Publicly Available Date Oct 11, 2022
Journal International Journal of Forecasting
Print ISSN 0169-2070
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 38
Issue 4
Pages 1519-1525
DOI https://doi.org/10.1016/j.ijforecast.2022.03.010
Public URL https://durham-repository.worktribe.com/output/1207807

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