Mean-based error measures for intermittent demand forecasting
By S. Prestwich, Roberto Rossi, S. Armagan Tarim, Brahim Hnich. 2013
53 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research
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In Economics and Econometrics
- Ruijin Du
- Minggang Wang
- H. Eugene Stanley
- Lin Chen
- Chao Wang
- Zhilin Qiao
- Alexei Botchkarev
- Ladislav Kristoufek
In Electrical and Electronic Engineering
In Management Science and Operations Research
Citations in the corpus
- Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market? (2018, methodology): “RMSE and MAPE have been widely used to evaluate the predictive accuracy [37], [38].”
- Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, background): “Second, so called, mean-based measures where error is calculated as 𝑒 = 𝐴̅ − 𝑃 𝑗 for evaluating forecasts against the mean of the underlying process of intermittent demand (Prestwich, et al, 2014).”