Forecasting with trees

Tim Januschowski · Kari Torkkola · Timo Erkkilä · Hilaf Hasson · Jan A. Gasthaus

117 citations0/0 files storedInternational Journal of Forecasting

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Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

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.

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References

2023Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics n.d.Forecasting principles and practice 2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks 2022Forecasting: theory and practice 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecasting 2019A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting 2019FFORMA: Feature-based forecast model averaging 2017A Multi-Horizon Quantile Recurrent Forecaster

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