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The Quest for a Better Forecast Error Metric: Measuring More than the Average Error

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  • Stefan de Kok

Abstract

In an article in the Summer 2006 issue of Foresight, Tom Willemain presented the argument that While most forecast-error metrics are averages of forecast errors, for intermittent demand series we should focus on the demand distribution and assess forecast error at each distinct level of demand. Accordingly, the appropriate accuracy metric will assess the difference between the actual and forecasted distributions of demand. The issue has not had much traction over the years since-except perhaps in energy studies -and we do not find error metrics based on full distributions present in forecasting support systems. Now Stefan de Kok picks up the argument and extends it to develop an error metric-Total Percentage Error-that measures the full range of uncertainty in our forecasts and, in doing so, both enables better inventory-planning and provides a more comprehensive way to gauge the quality of the forecast. Copyright International Institute of Forecasters, 2017

Suggested Citation

  • Stefan de Kok, 2017. "The Quest for a Better Forecast Error Metric: Measuring More than the Average Error," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 46, pages 36-45, Summer.
    • Handle: RePEc:for:ijafaa:y:2017:i:46:p:36-45
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    Cited by:

    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.

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