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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

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

Research output: Contribution to journalJournal article

Published

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Demand forecasting by temporal aggregation : using optimal or multiple aggregation levels? / Kourentzes, Nikolaos; Rostami-Tabar, Bahman; Barrow, Devon.

In: Journal of Business Research, Vol. 78, 09.2017, p. 1-9.

Research output: Contribution to journalJournal article

Harvard

Kourentzes, N, Rostami-Tabar, B & Barrow, D 2017, 'Demand forecasting by temporal aggregation: using optimal or multiple aggregation levels?', Journal of Business Research, vol. 78, pp. 1-9.

APA

Vancouver

Author

Kourentzes, Nikolaos ; Rostami-Tabar, Bahman ; Barrow, Devon. / Demand forecasting by temporal aggregation : using optimal or multiple aggregation levels?. In: Journal of Business Research. 2017 ; Vol. 78. pp. 1-9.

Bibtex

@article{f9903370f71d43f1a13000f0e7a4e9cd,
title = "Demand forecasting by temporal aggregation: using optimal or multiple aggregation levels?",
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.",
keywords = "Forecasting, Time Series, Temporal aggregation",
author = "Nikolaos Kourentzes and Bahman Rostami-Tabar and Devon Barrow",
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",
year = "2017",
month = "9",
language = "English",
volume = "78",
pages = "1--9",
journal = "Journal of Business Research",
issn = "0148-2963",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Demand forecasting by temporal aggregation

T2 - using optimal or multiple aggregation levels?

AU - Kourentzes, Nikolaos

AU - Rostami-Tabar, Bahman

AU - Barrow, Devon

N1 - 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

PY - 2017/9

Y1 - 2017/9

N2 - 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.

AB - 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.

KW - Forecasting

KW - Time Series

KW - Temporal aggregation

M3 - Journal article

VL - 78

SP - 1

EP - 9

JO - Journal of Business Research

JF - Journal of Business Research

SN - 0148-2963

ER -