Combining Forecasts: A Genetic Programming Approach

Adriano S. Koshiyama, Tatiana Escovedo, Douglas M. Dias, Marley M. B. R. Vellasco and Marco A. C. Pacheco
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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

International Journal of Natural Computing Research (IJNCR), 2012, vol. 3, issue 3, 41-58

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.

Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jncr00:v:3:y:2012:i:3:p:41-58

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