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Evaluating forecasts: a look at aggregate bias and accuracy measures

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  • Dean W. Wichern

    (Texas A&M University, USA)

  • Benito E. Flores

    (Texas A&M University, USA)

Abstract

In this paper an investigation is made of the properties and use of two aggregate measures of forecast bias and accuracy. These are metrics used in business to calculate aggregate forecasting performance for a family (group) of products. We find that the aggregate measures are not particularly informative if some of the one-step-ahead forecasts are biased. This is likely to be the case in practice if frequently employed forecasting methods are used to generate a large number of individual forecasts. In the paper, examples are constructed to illustrate some potential problems in the use of the metrics. We propose a simple graphical display of forecast bias and accuracy to supplement the information yielded by the accuracy measures. This support includes relevant boxplots of measures of individual forecasting success. This tool is simple but helpful as the graphic display has the potential to indicate forecast deterioration that can be masked by one or both of the aggregate metrics. The procedures are illustrated with data representing sales of food items. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Dean W. Wichern & Benito E. Flores, 2005. "Evaluating forecasts: a look at aggregate bias and accuracy measures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 433-451.
    • Handle: RePEc:jof:jforec:v:24:y:2005:i:6:p:433-451
    DOI: 10.1002/for.962
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    References listed on IDEAS

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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    2. Hyndman, Rob J. & Billah, Baki, 2003. "Unmasking the Theta method," International Journal of Forecasting, Elsevier, vol. 19(2), pages 287-290.
    3. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    4. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    5. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, vol. 9(4), pages 527-529, December.
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    Cited by:

    1. Wan, Xiang & Sanders, Nadia R., 2017. "The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration," International Journal of Production Economics, Elsevier, vol. 186(C), pages 123-131.

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