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

Soft Computing volume 16, pages 919–942 (2012)Cite this article

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.

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Notes

  1. National Statistics Institute (http://www.ine.es/).

  2. National Statistics Institute (http://www.ine.es/).

  3. The General Direction of Traffic (http://www.dgt.es/).

  4. http://www.neural-forecasting-competition.com/NN3/datasets.htm.

  5. The Ministry of Culture (http://www.mcu.es/).

  6. Available from http://pacific.commerce.ubc.ca/xr/data.html, thanks to the work done by Prof. Werner Antweiler, from the University of British Columbia, Vancouver, Canada.

  7. Spanish Association of Automobile and Truck Manufacturers (http://www.anfac.com/).

  8. The Ministry of Industry, Tourism and Trade (http://www.mityc.es/).

  9. The Ministry of Labour and Immigration (http://www.mtin.es/).

  10. The Ministry of Housing (http://www.mviv.es/).

  11. http://www.fomento.es/.

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Acknowledgments

This work has been supported by the Caja Rural de Jaen and the University of Jaen (Spain) UJA-08-16-30 project, the regional project TIC-3928 (Feder Founds), and the Spanish project TIN 2008-06681-C06-02 (Feder Founds).

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Authors and Affiliations

  1. Department of Computer Sciences, Campus Las Lagunillas s/n, 23071, Jaen, Spain

    E. Parras-Gutierrez, V. M. Rivas & M. J. del Jesus

  2. Department of Computers, Architecture and Technology, C/ Periodista Daniel Saucedo s/n, 18071, Granada, Spain

    M. Garcia-Arenas

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  1. E. Parras-Gutierrez
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Correspondence to V. M. Rivas.

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Parras-Gutierrez, E., Garcia-Arenas, M., Rivas, V.M. et al. Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm. Soft Comput 16, 919–942 (2012). https://doi.org/10.1007/s00500-011-0784-2

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Keywords

  • Neural networks
  • Coevolutionary algorithms
  • Time series forecasting
  • Significant lags