Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning method
Publication typeJournal article with impact factor
JournalInternational Journal of Forecasting
MetadataShow full item record
AbstractThe 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.
KeywordM5 Accuracy Competition, Computational Requirements, Transfer Learning, LightGBM, Hierarchical Forecasting
Knowledge Domain/IndustryOperations & Supply Chain Management