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

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    Publication type
    Journal article with impact factor
    Author
    Wellens, Arnoud P.
    Udenio, Maxi
    Boute, Robert
    Publication Year
    2022
    Journal
    International Journal of Forecasting
    Publication Volume
    38
    Publication Issue
    4
    4
    Publication Begin page
    1482
    Publication End page
    1491
    
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    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 Mcompetitions. Yet, large-scale adoption is hampered due to the signi cant 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 e ort in hierarchical forecasting and provide 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 ndings provide evidence for a novel application of TL to facilitate practical applicability of the M5 winning methods in large-scale settings with hierarchically structured data.
    Keyword
    M5 Accuracy Competition, Computational Requirements, Transfer Learning, LightGBM, Hierarchical Forecasting
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.ijforecast.2021.09.011
    URI
    http://hdl.handle.net/20.500.12127/6974
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ijforecast.2021.09.011
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