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  • Kourentzes 2017 Demand forecasting by temporal aggregation - using optimal or multiple aggregation levels

    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Business 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 Journal of Business Research, 78, 2017 DOI: 10.1016/j.jbusres.2017.04.016

    Accepted author manuscript, 308 KB, PDF document

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

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Demand forecasting by temporal aggregation: using optimal or multiple aggregation levels?

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>09/2017
<mark>Journal</mark>Journal of Business Research
Volume78
Number of pages9
Pages (from-to)1-9
Publication statusPublished
Early online date28/04/17
Original languageEnglish

Abstract

Recent advances have demonstrated the benefits of temporal aggregation for demand forecasting, including increased accuracy, improved stock control and reduced modelling uncertainty. With temporal aggregation a series is transformed, strengthening or attenuating different elements and thereby enabling better identification of the time series structure. Two different schools of thought have emerged. The first focuses on identifying a single optimal temporal aggregation level at which a forecasting model maximises its accuracy. In contrast, the second approach fits multiple models at multiple levels, each capable of capturing different features of the data. Both approaches have their merits, but so far they have been investigated in isolation. We compare and contrast them from a theoretical and an empirical perspective, discussing the merits of each, comparing the realised accuracy gains under different experimental setups, as well as the implications for business practice. We provide suggestions when to use each for maximising demand forecasting gains.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Journal of Business 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 Journal of Business Research, 78, 2017 DOI: 10.1016/j.jbusres.2017.04.016