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

Nikolaos Kourentzes, Bahman Rostami-Tabar, Devon K Barrow

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)
191 Downloads (Pure)


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.

Publisher Statement: NOTICE: 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

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Original languageEnglish
Pages (from-to)1-9
Number of pages10
JournalJournal of Business Research
Early online date28 Apr 2017
Publication statusPublished - Sept 2017


  • Forecasting
  • Demand planning
  • Temporal aggregation
  • Model selection
  • Exponential smoothing
  • MAPA


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