Wellens, Arnoud P.Udenio, MaxiBoute, Robert2021-10-052021-10-0520220169-207010.1016/j.ijforecast.2021.09.011http://hdl.handle.net/20.500.12127/6974The 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.enM5 Accuracy CompetitionComputational RequirementsTransfer LearningLightGBMHierarchical ForecastingInternational Journal of Forecasting102358