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Forecasting with trees

Author

Listed:
  • Januschowski, Tim
  • Wang, Yuyang
  • Torkkola, Kari
  • Erkkilä, Timo
  • Hasson, Hilaf
  • Gasthaus, Jan

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.

Suggested Citation

  • Januschowski, Tim & Wang, Yuyang & Torkkola, Kari & Erkkilä, Timo & Hasson, Hilaf & Gasthaus, Jan, 2022. "Forecasting with trees," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1473-1481.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:4:p:1473-1481
    DOI: 10.1016/j.ijforecast.2021.10.004
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    References listed on IDEAS

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    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
    3. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    4. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    5. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    6. Tim Januschowski & Stephan Kolassa, 2019. "A Classification of Business Forecasting Problems," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 52, pages 36-43, Winter.
    7. Spyros Makridakis & Evangelos Spiliotis, 2021. "The M5 Competition and the Future of Human Expertise in Forecasting," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 60, pages 33-37, Winter.
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    Cited by:

    1. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab, 2023. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Papers 2303.16149, arXiv.org.

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