A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

Kasun Bandara, Hansika Hewamalage, Rakshitha Godahewa and Puwasala Gamakumara

International Journal of Forecasting, 2022, vol. 38, issue 4, 1400-1404

Abstract: This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.

Keywords: M5 forecasting competition; Global forecasting models; Sales demand forecasting; LightGBM models; Pooled Regression models (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:1400-1404

DOI: 10.1016/j.ijforecast.2021.11.004

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