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Demand forecasting by temporal aggregation

Rostami-Tabar, Bahman ORCID:, Babai, M. Zied, Syntetos, Argyrios ORCID: and Ducq, Yves 2013. Demand forecasting by temporal aggregation. Naval Research Logistics 60 (6) , pp. 479-498. 10.1002/nav.21546

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Demand forecasting performance is subject to the uncertainty underlying the time series an organization is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lower-frequency “time buckets.” The approach under concern is termed to as temporal aggregation, and in this article, we investigate its impact on forecasting performance. We assume that the nonaggregated demand follows either a moving average process of order one or a first-order autoregressive process and a single exponential smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical mean-squared error expressions are derived for the aggregated and nonaggregated demand to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant, and the process parameters. Valuable insights are offered to practitioners and the article closes with an agenda for further research in this area.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > HD Industries. Land use. Labor
V Naval Science > V Naval Science (General)
Uncontrolled Keywords: demand forecasting; temporal aggregation; stationary processes; single exponential smoothing
Publisher: Wiley-Blackwell
ISSN: 0894-069X
Date of First Compliant Deposit: 22 February 2017
Last Modified: 10 Apr 2024 16:21

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