Simple averaging of direct and recursive forecasts via partial pooling using machine learning

YeonJun In and Jae-Yoon Jung

International Journal of Forecasting, 2022, vol. 38, issue 4, 1386-1399

Abstract: This article introduces the winning method at the M5 Accuracy competition. The presented method takes a simple manner of averaging the results of multiple base forecasting models that have been constructed via partial pooling of multi-level data. All base forecasting models of adopting direct or recursive multi-step forecasting methods are trained by the machine learning technique, LightGBM, from three different levels of data pools. At the competition, the simple averaging of the multiple direct and recursive forecasting models, called DRFAM, obtained the complementary effects between direct and recursive multi-step forecasting of the multi-level product sales to improve the accuracy and the robustness.

Keywords: Direct and recursive multi-step forecasting; Multi-level data; Forecast averaging; Machine learning; LightGBM (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:1386-1399

DOI: 10.1016/j.ijforecast.2021.11.007

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