Mean-Based Forecasting Error Measures for Intermittent Demand

Steven Prestwich, S Armagan Tarim, Roberto Rossi, Brahim Hnich

Research output: Contribution to journalArticlepeer-review


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 because of infinities, 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 almost no infinities, and with 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.
Original languageEnglish
Pages (from-to)6782-6791
JournalInternational Journal of Production Research
Issue number22
Early online date12 May 2014
Publication statusPublished - 17 Nov 2014


  • forcasting
  • intermittent demand
  • error measure


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