The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems

E. Parras-Gutierrez, V. M. Rivas, J. J. Merelo

Abstract

This paper presents an experimental study in which the effectiveness of the L-Co-R method is tested. L-Co-R is a co-evolutionary algorithm to time series forecasting that evolves, on one hand, RBFNs building an appropriate architecture of net, and on the other hand, sets of time lags that represents the time series in order to perform the forecasting using, at the same time, its own forecasted values. This coevolutive approach makes possible to divide the main problem into two subproblems where every individual of one population cooperates with the individuals of the other. The goal of this work is to analyze the results obtained by {\metodo} comparing with other methods from the time series forecasting field. For that, 20 time series and 5 different methods found in the literature have been selected, and 3 distinct quality measures have been used to show the results. Finally, a statistical study confirms the good results of L-Co-R in most cases.

References

  1. Araújo, R. (2010). A quantum-inspired evolutionary hybrid intelligent apporach fo stock market prediction. International Jorunal of Intelligent Computing and Cybernetics, 3(10):24-54.
  2. Bowerman, B., O'Connell, R., and Koehler, A. (2004). Forecasting: methods and applications. Thomson Brooks/Cole: Belmont, CA.
  3. Box, G. and Jenkins, G. (1976). Time series analysis: forecasting and control. San Francisco: Holden Day.
  4. Broomhead, D. and Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, 2:321-355.
  5. Castillo, P., Arenas, M., Merelo, J., and Romero, G. (2003). Cooperative co-evolution of multilayer perceptrons. In Mira, J. and Í lvarez, J. R., editors, Computational Methods in Neural Modeling, volume 2686 of Lecture Notes in Computer Science, pages 358-365. Springer Berlin Heidelberg.
  6. Clements, M., Franses, P., and Swanson, N. (2004). Forecasting economic and financial time-series with nonlinear models. International Journal of Forecasting, 20(2):169-183.
  7. Du, H. and Zhang, N. (2008). Time series prediction using evolving radial basis function networks with new encoding scheme. Neurocomputing, 71(7-9):1388- 1400.
  8. Eshelman, L. (1991). The chc adptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In Proceedings of 1st Workshop on Foundations of Genetic Algorithms, pages 265-283.
  9. García-Pedrajas, N., Hervas-Martínez, C., and Ortiz-Boyer, D. (2005). Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Transactions on Evolutionary Computation, 9(3):271-302.
  10. Harpham, C. and Dawson, C. (2006). The effect of different basis functions on a radial basis function network for time series prediction: A comparative study. Neurocomputing, 69(16-18):2161-2170.
  11. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2):65-70.
  12. Hyndman, R. and Koehler, A. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4):679-688.
  13. Hyndman, R. J. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for r. Journal of Statistical Software, 27(3):1-22.
  14. Jain, A. and Kumar, A. (2007). Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7(2):585-592.
  15. Li, M., Tian, J., and Chen, F. (2008). Improving multiclass pattern recognition with a co-evolutionary rbfnn. Pattern Recognition Letters, 29(4):392-406.
  16. Lukoseviciute, K. and Ragulskis, M. (2010). Evolutionary algorithms for the selection of time lags for time series forecasting by fuzzy inference systems. Neurocomputing, 73(10-12):2077-2088.
  17. Ma, X. and Wu, H. (2010). Power system short-term load forecasting based on cooperative co-evolutionary immune network model. In Proceedings of 2nd International Conference on Education Technology and Computer, pages 582-585.
  18. Makridakis, S. and Hibon, M. (2000). The m3-competition: results, conclusions and implications. International Journal of Forecasting, 16(4):451-476.
  19. Maus, A. and Sprott, J. C. (2011). Neural network method for determining embedding dimension of a time series. Communications in Nonlinear Science and Numerical Simulation, 16(8):3294-3302.
  20. Parras-Gutierrez, E., Garcia-Arenas, M., Rivas, V., and del Jesus, M. (2012). Coevolution of lags and rbfns for time series forecasting: L-co-r algorithm. Soft Computing, 16(6):919-942.
  21. Pen˜a, D. (2005). Análisis de Series Temporales. Alianza Editorial.
  22. Potter, M. and De Jong, K. (1994). A cooperative coevolutionary approach to function optimization. In Proceedings of Parallel Problem Solving from Nature, volume 866 of Lecture Notes in Computer Science, pages 249-257. Springer Berlin/Heidelberg.
  23. Rivas, V., Merelo, J., Castillo, P., Arenas, M., and Castellano, J. (2004). Evolving rbf neural networks for timeseries forecasting with evrbf. Information Sciences, 165(3-4):207 - 220.
  24. Sheskin, D. (2004). Handbook of parametric and nonparametric statistical procedures. Chapman & Hall/CRC.
  25. Snyder, R. (1985). Recursive estimation of dynamic linear models. Journal of the Royal Statistical Society. Series B (Methodological), 47(2):272-276.
  26. Takens, F. (1980). Dynamical Systems and Turbulence, Lecture Notes In Mathematics, volume 898, chapter Detecting strange attractor in turbulence, pages 366-381. Springer, New York, NY.
  27. Tong, H. (1978). On a threshold model. Pattern recognition and signal processing, NATO ASI Series E: Applied Sc., 29:575-586.
  28. Winters, P. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3):324-342.
Download


Paper Citation


in Harvard Style

Parras-Gutierrez E., M. Rivas V. and Merelo J. (2013). The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 144-151. DOI: 10.5220/0004555101440151


in Bibtex Style

@conference{ecta13,
author={E. Parras-Gutierrez and V. M. Rivas and J. J. Merelo},
title={The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},
year={2013},
pages={144-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004555101440151},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - The L-Co-R Co-evolutionary Algorithm - A Comparative Analysis in Medium-term Time-series Forecasting Problems
SN - 978-989-8565-77-8
AU - Parras-Gutierrez E.
AU - M. Rivas V.
AU - Merelo J.
PY - 2013
SP - 144
EP - 151
DO - 10.5220/0004555101440151