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Mean-Based Error Measures for Intermittent Demand Forecasting


Steven Prestwich, Roberto Rossi, Armagan Tarim, Brahim Hnich

Publication Type: 
Refereed Original Article
To compare different forecasting methods on demand series we require an error measure. Many error measures have been proposed, but when demand is intermittent some become inapplicable, some give counter-intuitive results, and there is no agreement on which is best. We argue that almost all known measures rank forecasters incorrectly on intermittent demand series. We propose several new error measures with wider applicability, and correct forecaster ranking on several intermittent demand patterns. We call these "mean-based" error measures because they evaluate forecasts against the (possibly time-dependent) mean of the underlying stochastic process instead of point demands.
Digital Object Identifer (DOI):
Publication Status: 
In Press
Date Accepted for Publication: 
Wednesday, 14 May, 2014
Publication Date: 
International Journal of Production Research
National University of Ireland, Dublin (UCD)
Open access repository: