Fathoming empirical forecasting competitions’ winners

Azzam Alroomi, Georgios Karamatzanis, Konstantinos Nikolopoulos, Anna Tilba and Shujun Xiao

International Journal of Forecasting, 2022, vol. 38, issue 4, 1519-1525

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?

Keywords: Forecasting; Competitions; Performance; Machine learning; Benchmarks (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1519-1525

DOI: 10.1016/j.ijforecast.2022.03.010

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