Home > Research > Publications & Outputs > Retail sales forecasting with meta-learning

Electronic data

  • Retail sales forecasting with meta learning_0329

    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 288 (1), 2020 DOI: 10.1016/j.ejor.2020.05.038

    Accepted author manuscript, 1.29 MB, PDF document

    Embargo ends: 28/05/22

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Retail sales forecasting with meta-learning

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/01/2021
<mark>Journal</mark>European Journal of Operational Research
Issue number1
Volume288
Number of pages18
Pages (from-to)111-128
Publication StatusPublished
Early online date28/05/20
<mark>Original language</mark>English

Abstract

Retail sales forecasting often requires forecasts for thousands of products for many stores. We present a meta-learning framework based on newly developed deep convolutional neural networks, which can first learn a feature representation from raw sales time series data automatically, and then link the learnt features with a set of weights which are used to combine a pool of base-forecasting methods. The experiments which are based on IRI weekly data show that the proposed meta-learner provides superior forecasting performance compared with a number of state-of-art benchmarks, though the accuracy gains over some more sophisticated meta ensemble benchmarks are modest and the learnt features lack interpretability. When designing a meta-learner in forecasting retail sales, we recommend building a pool of base-forecasters including both individual and pooled forecasting methods, and target finding the best combination forecasts instead of the best individual method.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 288 (1), 2020 DOI: 10.1016/j.ejor.2020.05.038