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Evaluation of Aggregate and Individual Forecast Method Selection Rules

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  • Robert Fildes

    (Manchester Business School, Booth St. West, Manchester, M15 6PB, England)

Abstract

A major use of univariate forecasting methods lies in production control where there is a large number of series to be forecast. The appropriate choice of forecasting method has the potential for major cost savings through improved accuracy. Where a new method is to be compared to one already established two distinct approaches to selecting between the two can be considered: aggregate selection, where a single method is applied to all the time series, versus individual selection, where the particular method appropriate for each series is identified and used to forecast future observations for that series. This paper evaluates these two selection rules using 263 data series from a single organization. The results show the potential of "individual selection" and also the difficulty of attaining it. For short lead times "aggregate selection" achieves similar accuracy. For longer leads it is outperformed by "individual selection" and also undermined by sampling variability. As a consequence, "aggregate selection" must be carried out across a wide cross-section of series and across time. When this is done the results of this case study show that "aggregate selection" is both simpler than and of comparable accuracy to "individual selection."

Suggested Citation

  • Robert Fildes, 1989. "Evaluation of Aggregate and Individual Forecast Method Selection Rules," Management Science, INFORMS, vol. 35(9), pages 1056-1065, September.
    • Handle: RePEc:inm:ormnsc:v:35:y:1989:i:9:p:1056-1065
    DOI: 10.1287/mnsc.35.9.1056
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    Cited by:

    1. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
    2. Robert Fildes & Gary Madden & Joachim Tan, 2007. "Optimal forecasting model selection and data characteristics," Applied Financial Economics, Taylor & Francis Journals, vol. 17(15), pages 1251-1264.
    3. Dimitrios Sarris & Evangelos Spiliotis & Vassilios Assimakopoulos, 2020. "Exploiting resampling techniques for model selection in forecasting: an empirical evaluation using out-of-sample tests," Operational Research, Springer, vol. 20(2), pages 701-721, June.
    4. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    5. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    6. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    7. Vokurka, Robert J. & Flores, Benito E. & Pearce, Stephen L., 1996. "Automatic feature identification and graphical support in rule-based forecasting: a comparison," International Journal of Forecasting, Elsevier, vol. 12(4), pages 495-512, December.
    8. Erjiang E & Ming Yu & Xin Tian & Ye Tao, 2022. "Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    9. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    10. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
    11. Rossetti Renato, 2019. "Forecasting the Sales of Console Games for the Italian Market," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(3), pages 76-88, September.
    12. Barrow, Devon K. & Kourentzes, Nikolaos, 2016. "Distributions of forecasting errors of forecast combinations: Implications for inventory management," International Journal of Production Economics, Elsevier, vol. 177(C), pages 24-33.
    13. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    14. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    15. Fotios Petropoulos & Enno Siemsen, 2023. "Forecast Selection and Representativeness," Management Science, INFORMS, vol. 69(5), pages 2672-2690, May.
    16. Haiyan Song, 1995. "A time-varying parameter consumption model for the UK," Applied Economics Letters, Taylor & Francis Journals, vol. 2(10), pages 339-342.
    17. Haiyan Song & Peter Romilly & Xiaming Liu, 1998. "The UK consumption function and structural instability: improving forecasting performance using a time-varying parameter approach," Applied Economics, Taylor & Francis Journals, vol. 30(7), pages 975-983.
    18. Shah, Chandra, 1997. "Model selection in univariate time series forecasting using discriminant analysis," International Journal of Forecasting, Elsevier, vol. 13(4), pages 489-500, December.
    19. Brown, Jane P. & Song, Haiyan & McGillivray, Alan, 1997. "Forecasting UK house prices: A time varying coefficient approach," Economic Modelling, Elsevier, vol. 14(4), pages 529-548, October.
    20. Gardner Jr., Everette S. & Diaz-Saiz, Joaquin, 2008. "Exponential smoothing in the telecommunications data," International Journal of Forecasting, Elsevier, vol. 24(1), pages 170-174.
    21. Tashman, Leonard J. & Kruk, Joshua M., 1996. "The use of protocols to select exponential smoothing procedures: A reconsideration of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 12(2), pages 235-253, June.
    22. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.

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