Another Look at Forecast Accuracy Metrics for Intermittent Demand

2006-01-01article270 citationsclosed

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Forecasting Techniques and Applications Time Series Analysis and Forecasting Statistical and numerical algorithms

Abstract

Preview: Some traditional measurements of forecast accuracy are unsuitable for intermittent-demand data because they can give infinite or undefined values. Rob Hyndman summarizes these forecast accuracy metrics and explains their potential failings. He also introduces a new metric—the mean absolute scaled error (MASE)—which is more appropriate for intermittent-demand data. More generally, he believes that the MASE should become the standard metric for comparing forecast accuracy across multiple time series.

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References

2006Another look at measures of forecast accuracy

1993Forecasting methods and applications

2000The M3-Competition: results, conclusions and implications

1992Error measures for generalizing about forecasting methods: Empirical comparisons

2004The accuracy of intermittent demand estimates

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