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Fathoming empirical forecasting competitions’ winners

Alroomi, Azzam and Karamatzanis, George and Nikolopoulos, Kostas and Tilba, Anna and Xiao, Shujun (2022) 'Fathoming empirical forecasting competitions’ winners.', International Journal of Forecasting, 38 (4). pp. 1519-1525.


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?

Item Type:Article
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Publisher statement:© 2022 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (
Date accepted:31 March 2022
Date deposited:22 April 2022
Date of first online publication:05 October 2022
Date first made open access:11 October 2022

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