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  • Kourentzes 2020 Elucidate_structure_in_intermittent_demand_series

    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, 2021 DOI: 10.1016/j.ejor.2020.05.046

    Accepted author manuscript, 1.06 MB, PDF document

    Embargo ends: 30/05/22

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

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Elucidate structure in intermittent demand series

Research output: Contribution to journalJournal articlepeer-review

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<mark>Journal publication date</mark>1/01/2021
<mark>Journal</mark>European Journal of Operational Research
Issue number1
Volume288
Number of pages12
Pages (from-to)141-152
Publication StatusPublished
Early online date30/05/20
<mark>Original language</mark>English

Abstract

Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.

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, 2021 DOI: 10.1016/j.ejor.2020.05.046