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Advantages of the MAD/Mean Ratio over the MAPE


  • Stephan Kolassa
  • Wolfgang Schütz


Stephan Kolassa and Wolfgang Schütz provide a careful look at the ratio MAD/Mean, which has been proposed as a substitute metric for the MAPE in the case of intermittent demand series. They explain how MAD/Mean can be viewed as a weighted mean of absolute percentage errors and thus as a weighted alternative to MAPE. They describe several advantages of MAD/Mean to the MAPE including applicability to inventory decisions, absence of bias in method selection, and suitability for series with intermittent as well as near-zero demands. Copyright International Institute of Forecasters, 2007

Suggested Citation

  • Stephan Kolassa & Wolfgang Schütz, 2007. "Advantages of the MAD/Mean Ratio over the MAPE," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 40-43, Spring.
  • Handle: RePEc:for:ijafaa:y:2007:i:6:p:40-43

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    Cited by:

    1. Prestwich, S.D. & Tarim, S.A. & Rossi, R. & Hnich, B., 2014. "Forecasting intermittent demand by hyperbolic-exponential smoothing," International Journal of Forecasting, Elsevier, vol. 30(4), pages 928-933.
    2. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    3. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    4. Sara J. Germain & James A. Lutz, 2020. "Climate extremes may be more important than climate means when predicting species range shifts," Climatic Change, Springer, vol. 163(1), pages 579-598, November.
    5. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    6. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    7. Kourentzes, Nikolaos & Petropoulos, Fotios, 2016. "Forecasting with multivariate temporal aggregation: The case of promotional modelling," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 145-153.
    8. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    9. Gorr, Wilpen L., 2009. "Forecast accuracy measures for exception reporting using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(1), pages 48-61.

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