A state space model for exponential smoothing with group seasonality

Pim Ouwehand, Rob Hyndman, Ton G. de Kok and Karel H. van Donselaar

No 7/07, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We present an approach to improve forecast accuracy by simultaneously forecasting a group of products that exhibit similar seasonal demand patterns. Better seasonality estimates can be made by using information on all products in a group, and using these improved estimates when forecasting at the individual product level. This approach is called the group seasonal indices (GSI) approach, and is a generalization of the classical Holt-Winters procedure. This article describes an underlying state space model for this method and presents simulation results that show when it yields more accurate forecasts than Holt-Winters.

Keywords: Common seasonality; demand forecasting; exponential smoothing; Holt-Winters; state space model. (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2007-06
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2007/wp7-07.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2007-7

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().