This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

A state space model for exponential smoothing with group seasonality

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Pim Ouwehand
Rob J. Hyndman ()
Ton G. de Kok
Karel H. van Donselaar

Additional information is available for the following registered author(s):

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/2007/wp7-07.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/07.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 26 pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:msh:ebswps:2007-7

Contact details of provider:
Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Phone: +61-3-9905-2489
Fax: +61-3-9905-5474
Email:
Web page: http://www.buseco.monash.edu.au/depts/ebs/
More information through EDIRC

Order Information:
Email:
Web: http://www.buseco.monash.edu.au/depts/ebs/pubs/wpapers/

For technical questions regarding this item, or to correct its listing, contact: (Simone Grose).

Related research
Keywords: Common seasonality; demand forecasting; exponential smoothing; Holt-Winters; state space model.;

Other versions of this item:

Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Archibald, Blyth C. & Koehler, Anne B., 2003. "Normalization of seasonal factors in Winters' methods," International Journal of Forecasting, Elsevier, vol. 19(1), pages 143-148. [Downloadable!] (restricted)
  2. Bunn, Derek W. & Vassilopoulos, Angelos I., 1999. "Comparison of seasonal estimation methods in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 15(4), pages 431-443, October. [Downloadable!] (restricted)
  3. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286. [Downloadable!] (restricted)
    Other versions:
  4. Bunn, Derek W. & Vassilopoulos, A. I., 1993. "Using group seasonal indices in multi-item short-term forecasting," International Journal of Forecasting, Elsevier, vol. 9(4), pages 517-526, December. [Downloadable!] (restricted)
  5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688. [Downloadable!] (restricted)
    Other versions:
  6. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
  7. Withycombe, Richard, 1989. "Forecasting with combined seasonal indices," International Journal of Forecasting, Elsevier, vol. 5(4), pages 547-552. [Downloadable!] (restricted)
  8. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July. [Downloadable!] (restricted)
  9. Anne B. Koehler & Rob J. Hyndman & Ralph D. Snyder & J. Keith Ord, 2005. "Prediction intervals for exponential smoothing using two new classes of state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 17-37. [Downloadable!]
Full references

Statistics
Access and download statistics

Did you know? RePEc data is maintained by each archive holder on its own website. Nothing is held centrally.

This page was last updated on 2009-11-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.