Soft Computing

, Volume 16, Issue 6, pp 919–942 | Cite as

Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm

  • E. Parras-Gutierrez
  • M. Garcia-Arenas
  • V. M. Rivas
  • M. J. del Jesus
Original Paper

Abstract

This paper introduces Lags COevolving with Rbfns (L-Co-R), a coevolutionary method developed to face time-series forecasting problems. L-Co-R simultaneously evolves the model that provides the forecasted values and the set of time lags the model must use in the prediction process. Coevolution takes place by means of two populations that evolve at the same time, cooperating between them; the first population is composed of radial basis function neural networks; the second one contains the individuals representing the sets of lags. Thus, the final solution provided by the method comprises both the neural net and the set of lags that better approximate the time series. The method has been tested across 34 different time series datasets, and the results compared to 6 different methods referenced in literature, and with respect to 4 different error measures. The results show that L-Co-R outperforms the rest of methods, as the statistical analysis carried out indicates.

Keywords

Neural networks Coevolutionary algorithms Time series forecasting Significant lags 

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • E. Parras-Gutierrez
    • 1
  • M. Garcia-Arenas
    • 2
  • V. M. Rivas
    • 1
  • M. J. del Jesus
    • 1
  1. 1.Department of Computer SciencesJaenSpain
  2. 2.Department of Computers, Architecture and TechnologyGranadaSpain

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