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

Author

Listed:
  • Bandara, Kasun
  • Hewamalage, Hansika
  • Godahewa, Rakshitha
  • Gamakumara, Puwasala

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.

Suggested Citation

  • Bandara, Kasun & Hewamalage, Hansika & Godahewa, Rakshitha & Gamakumara, Puwasala, 2022. "A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1400-1404.
    • Handle: RePEc:eee:intfor:v:38:y:2022:i:4:p:1400-1404
    DOI: 10.1016/j.ijforecast.2021.11.004
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    References listed on IDEAS

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    1. Montero-Manso, Pablo & Hyndman, Rob J., 2021. "Principles and algorithms for forecasting groups of time series: Locality and globality," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1632-1653.
    2. Juan R Trapero & Nikolaos Kourentzes & Robert Fildes, 2015. "On the identification of sales forecasting models in the presence of promotions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 299-307, February.
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    More about this item

    Keywords

    M5 forecasting competition; Global forecasting models; Sales demand forecasting; LightGBM models; Pooled Regression models;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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    Access and download statistics

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