Fathoming empirical forecasting competitions’ winners

Alroomi, Azzam, Karamatzanis, Georgios, Nikolopoulos, Konstantinos, Tilba, Anna and Xiao, Shujun (2022) Fathoming empirical forecasting competitions’ winners. International Journal of Forecasting, 38 (4). pp. 1519-1525. ISSN 0169-2070

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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. Regardless, this result 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 we can argue that this will 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
Uncontrolled Keywords:	benchmarks,competitions,forecasting,machine learning,performance,business and international management ,/dk/atira/pure/subjectarea/asjc/1400/1403
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	04 Mar 2024 18:39
Last Modified:	14 Oct 2025 10:34
URI:	https://ueaeprints.uea.ac.uk/id/eprint/94558
DOI:	10.1016/j.ijforecast.2022.03.010

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