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

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

International Journal of Forecasting, 2022, vol. 38, issue 4, 1482-1491

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.

Keywords: M5 Accuracy competition; Computational requirements; Transfer learning; LightGBM; Hierarchical forecasting (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:4:p:1482-1491

DOI: 10.1016/j.ijforecast.2021.09.011

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