Stochastic models underlying Croston's method for intermittent demand forecasting
Lydia Shenstone and
Rob Hyndman
No 1/03, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
Abstract:
Intermittent demand commonly occurs with inventory data, with many time periods having no demand and small demand in the other periods. Croston's method is a widely used procedure for intermittent demand forecasting. However, it is an ad hoc method with no properly formulated underlying stochastic model. In this paper, we explore possible models underlying Croston's method and three related methods, and we show that any underlying model will be inconsistent with the properties of intermittent demand data. However, we find that the point forecasts and prediction intervals based on such underlying models may still be useful.
Keywords: Croston's method; exponential smoothing; forecasting; intermittent demand. (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2003-02
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2003/wp1-03.pdf (application/pdf)
Related works:
Journal Article: Stochastic models underlying Croston's method for intermittent demand forecasting (2005) 
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:2003-1
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 ().