Forecasting with trees

Tim Januschowski, Yuyang Wang, Kari Torkkola, Timo Erkkilä, Hilaf Hasson and Jan Gasthaus

International Journal of Forecasting, 2022, vol. 38, issue 4, 1473-1481

Abstract: The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition. This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks.

Keywords: Random forests; Probabilistic forecasting; Gradient Boosted Trees; Global forecasting models; Deep Learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1473-1481

DOI: 10.1016/j.ijforecast.2021.10.004

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