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

Kourentzes, Nikolaos and Rostami-Tabar, Bahman and Barrow, Devon (2017) Demand forecasting by temporal aggregation:using optimal or multiple aggregation levels? Journal of Business Research, 78. pp. 1-9. ISSN 0148-2963

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Business Research
Additional Information:
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
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1406
Subjects:
ID Code:
85973
Deposited By:
Deposited On:
25 Apr 2017 09:28
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Nov 2020 04:36