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Advantages of the MAD/Mean Ratio over the MAPE

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  • Stephan Kolassa
  • Wolfgang Schütz

Abstract

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. 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.
    2. Athanasopoulos, George & Kourentzes, Nikolaos, 2023. "On the evaluation of hierarchical forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1502-1511.
    3. 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.
    4. 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.
    5. 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.
    6. Tom Wilson, 2022. "Preparing local area population forecasts using a bi-regional cohort-component model without the need for local migration data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 46(32), pages 919-956.
    7. Prestwich, S.D. & Tarim, S.A. & Rossi, R., 2021. "Intermittency and obsolescence: A Croston method with linear decay," International Journal of Forecasting, Elsevier, vol. 37(2), pages 708-715.
    8. Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
    9. Ducharme, Corey & Agard, Bruno & Trépanier, Martin, 2021. "Forecasting a customer's Next Time Under Safety Stock," International Journal of Production Economics, Elsevier, vol. 234(C).
    10. 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.
    11. 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.
    12. Philip Rees & Tom Wilson, 2023. "Accuracy of Local Authority Population Forecasts Produced by a New Minimal Data Model: A Case Study of England," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(6), pages 1-30, December.
    13. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    14. Marlon Schlemminger & Raphael Niepelt & Rolf Brendel, 2021. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles," Energies, MDPI, vol. 14(8), pages 1-24, April.
    15. Wolters, Jannik & Huchzermeier, Arnd, 2021. "Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment," Journal of Retailing, Elsevier, vol. 97(4), pages 726-745.
    16. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    17. 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.
    18. 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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