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Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts

Author

Listed:
  • José V. Segura-Heras

    (I.U. Operations Research Center, University Miguel Hernandez of Elche, Avda. Ferrocarril s/n, 03202 Elche, Spain)

  • José D. Bermúdez

    (Department of Statistics and O.R., University of Valencia, C/ Dr. Moliner 50, 46100 Burjassot, Spain)

  • Ana Corberán-Vallet

    (Department of Statistics and O.R., University of Valencia, C/ Dr. Moliner 50, 46100 Burjassot, Spain)

  • Enriqueta Vercher

    (Department of Statistics and O.R., University of Valencia, C/ Dr. Moliner 50, 46100 Burjassot, Spain)

Abstract

This paper deals with the weighted combination of forecasting methods using intelligent strategies for achieving accurate forecasts. In an effort to improve forecasting accuracy, we develop an algorithm that optimizes both the methods used in the combination and the weights assigned to the individual forecasts, COmbEB. The performance of our procedure can be enhanced by analyzing separately seasonal and non-seasonal time series. We study the relationships between prediction errors in the validation set and those of ex-post forecasts for different planning horizons. This study reveals the importance of setting the size of the validation set in a proper way. The performance of the proposed strategy is compared with that of the best prediction strategy in the analysis of each of the 100,000 series included in the M4 Competition.

Suggested Citation

  • José V. Segura-Heras & José D. Bermúdez & Ana Corberán-Vallet & Enriqueta Vercher, 2022. "Analysis of Weighting Strategies for Improving the Accuracy of Combined Forecasts," Mathematics, MDPI, vol. 10(5), pages 1-12, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:725-:d:757941
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    References listed on IDEAS

    as
    1. Zou, Hui & Yang, Yuhong, 2004. "Combining time series models for forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 69-84.
    2. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    3. J. Bermúdez & J. Segura & E. Vercher, 2008. "SIOPRED: a prediction and optimisation integrated system for demand," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 258-271, December.
    4. J D Bermúdez & J V Segura & E Vercher, 2006. "Improving demand forecasting accuracy using nonlinear programming software," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 94-100, January.
    5. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    6. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    7. J. D. Bermudez & J. V. Segura & E. Vercher, 2007. "Holt-Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1075-1090.
    8. Everette S. Gardner, Jr. & Ed. Mckenzie, 1985. "Forecasting Trends in Time Series," Management Science, INFORMS, vol. 31(10), pages 1237-1246, October.
    9. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    10. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    11. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    12. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    13. E. Vercher & A. Corberán-Vallet & J. Segura & J. Bermúdez, 2012. "Initial conditions estimation for improving forecast accuracy in exponential smoothing," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 517-533, July.
    14. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
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    More about this item

    Keywords

    forecasting; time series methods; forecasting combination; M4 Competition;
    All these keywords.

    JEL classification:

    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting

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