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

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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.(c) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Publisher
ELSEVIER
Issue Date
2022-10
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF FORECASTING, v.38, no.4, pp.1386 - 1399

ISSN
0169-2070
DOI
10.1016/j.ijforecast.2021.11.007
URI
http://hdl.handle.net/10203/299580
Appears in Collection
RIMS Journal Papers
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