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

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

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.

Suggested Citation

  • Wellens, Arnoud P. & Udenio, Maxi & Boute, Robert N., 2022. "Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1482-1491.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:4:p:1482-1491
    DOI: 10.1016/j.ijforecast.2021.09.011
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    More about this item

    Keywords

    M5 Accuracy competition; Computational requirements; Transfer learning; LightGBM; Hierarchical forecasting;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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