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Combining Forecasts: A Genetic Programming Approach

Author

Listed:
  • Adriano S. Koshiyama

    (Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil)

  • Tatiana Escovedo

    (Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil)

  • Douglas M. Dias

    (Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil)

  • Marley M. B. R. Vellasco

    (Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil)

  • Marco A. C. Pacheco

    (Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil)

Abstract

Combining forecasts is a common practice in time series analysis. This technique involves weighing each estimate of different models in order to minimize the error between the resulting output and the target. This work presents a novel methodology, aiming to combine forecasts using genetic programming, a metaheuristic that searches for a nonlinear combination and selection of forecasters simultaneously. To present the method, the authors made three different tests comparing with the linear forecasting combination, evaluating both in terms of RMSE and MAPE. The statistical analysis shows that the genetic programming combination outperforms the linear combination in two of the three tests evaluated.

Suggested Citation

  • Adriano S. Koshiyama & Tatiana Escovedo & Douglas M. Dias & Marley M. B. R. Vellasco & Marco A. C. Pacheco, 2012. "Combining Forecasts: A Genetic Programming Approach," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 3(3), pages 41-58, July.
    • Handle: RePEc:igg:jncr00:v:3:y:2012:i:3:p:41-58
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    Cited by:

    1. Hoque, Jawad Mahmud & Erhardt, Gregory D. & Schmitt, David & Chen, Mei & Wachs, Martin, 2021. "Estimating the uncertainty of traffic forecasts from their historical accuracy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 339-349.

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