Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods

Arnoud P. Wellens*, Maxi Udenio, Robert N. Boute

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The winning machine learning methods of the M5 Accuracy competition demonstrated high levels of forecast accuracy compared to the top-performing benchmarks in the history of the M-competitions. Yet, large-scale adoption is hampered due to the significant computational requirements to model, tune, and train these state-of-the-art algorithms. To overcome this major issue, we discuss the potential of transfer learning (TL) to reduce the computational effort in hierarchical forecasting and provide a proof of concept that TL can be applied on M5 top-performing methods. We demonstrate our easy-to-use TL framework on the recursive store-level LightGBM models of the M5 winning method and attain similar levels of forecast accuracy with roughly 25% less training time. Our findings provide evidence for a novel application of TL to facilitate the practical applicability of the M5 winning methods in large-scale settings with hierarchically structured data.

Original languageEnglish (US)
JournalInternational Journal of Forecasting
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • Computational requirements
  • Hierarchical forecasting
  • LightGBM
  • M5 Accuracy competition
  • Transfer learning

ASJC Scopus subject areas

  • Business and International Management

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