Time series forecasting: Automatic determination of lags and radial basis neural networks for a changing horizon environment

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2010, Pages

  Parras Gutierrez, E ^a   Rivas, V M ^a  

^a UNIVERSITY OF JAÉN   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY ALGORITHMS; FORECASTING; RADIAL BASIS FUNCTION NETWORKS;

AUTOMATIC DETERMINATION; FORECAST TIME; META-EVOLUTIONARY; ONE-TIME; RADIAL BASIS FUNCTION NEURAL NETWORKS (RBF); RADIAL BASIS NEURAL NETWORKS; TIME SERIES FORECASTING; TIME SERIES PREDICTION; TIMES SERIES;

TIME SERIES;

EID: 79959468164     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2010.5596797     Document Type: Conference Paper

References (24)

1. G. Box and G. Jenkins. Time series analysis, forecasting and control. Holden-Day, Incorporated, 1976.
2. D. Broomhead and D. Lowe. Multivariable Functional Interpolation and Adaptative Networks. Complex Systems, 11:321-355, 1988.
3. P. Caire, G. Hatabian, and C. Muller. Progress in forecasting by neural networks. In Proceedings of the International Joint Conference on Neural Networks, pages 540-545, 1992.
4. B. Carse and T. Fogarty. Fast evolutionary learning of minimal radial basis function neural networks using a genetic algorithm. In Proceedings of the Evolutionary Computing, Springer-Verlag, pages 1-22, 1996.
5. L.J. Eshelman L.J. The CHC adptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In First Workshop on Foundations of Genetic Algorithms. Morgan Kaufmann, pp. 265-283, 1991.
6. R. Frank, N. Davey, and S. Hunt. Time series prediction and neural networks. Journal of Intelligent and Robotic Systems, 31(2):91-103, 2001. (Pubitemid 33098999)
7. J. Gonzalez. Identificacion y optimizacion de redes de funciones base radiales para aprimacion funcional. University of Granada, 2001.
8. C. Granger and T. Terasvirta. Modelling non-linear economic relationships. Oxford University Press, 1993.
9. R. Jones, Y. Lee, and C. B. et al. Function approximation and time series prediction with neural networks. Proceedings of the IEEE International Joint Conference on Neural Networks, 1:649-665, 1990.
10. E. Keogh. On the need for time series data mining benchmarks: A survey and empirical. Data Mining and Knowledge Discovery, 7(4):349-371, 2003.
11. A.C. Martinez-Estudillo, F.J. Martinez-Estudillo, C. Hervas-Martinez, N. Garcia. Evolutionary Product Unit based Neural Networks for Regression. Data Mining and Knowledge Discovery, Neural Networks 19(4):477-486, 2006. (Pubitemid 43850240)
12. J. Platt. Learning by combining memorization and gradient descent. Advances in Neural Information Processing Systems, 3, 1991.
13. W. Press, S. Teukolsky, W. Vetterling, and B. Flannery. Numerical Recipes in C. Cambridge University Press, 2nd edition, 1992.
14. E. Parras-Gutirrez, V.M. Rivas, M.J. del Jesus, E-tsRBF: preliminary results on the simultaneous determination of time-lags and parameters of Radial Basis Function Neural Networks for time series forecasting. 9th International Conference on Intelligent Systems Design and Applications, Pisa (Italy), pp. 1445-1449, 2009.
15. V. Rivas, J. Merelo, P. Castillo, M. Arenas, and J. Castellanos. Evolving rbf neural networks for time-series forecasting with evrbf. Information Sciences, 165(3-4):207-220, 2004.
16. V. Rivas, I. Garcia-Arenas, J. Merelo, and A. Prieto. Evrbf: Evolving rbf neural networks for classification problems. Proceedings of the International Conference on Applied Informatics and Communications (AIC07), pages 100-106, 2007.
17. A. Rivera, I. Rojas, J. Ortega, and M. del Jesus. A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks. Soft Computing, 2006.
18. G. Rupert and J. Miller. Basics of applied statistics. Statistical Science Series, page 336, 1997.
19. A. Sheta and K. D. Jong. Time-series forecasting using ga-tuned radial basis functions. Information Sciences, 133(3-4):221-228, 2001.
20. L.X. Wang, J.M. Mendel. Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man and Cybernetics 22:6 1414-1427, 1992.
21. B. Whitehead and T. Choate. Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Transactions on Neural Networks, 7(4):869-880, July 1996. (Pubitemid 126776360)
22. D. Wichern and R. Jones. Assessing the impact of market disturbances using intervention analysis. Management Science, 24:329-337, 1977.
23. F. Wilcoxon. Individual comparisons by ranking methods. Biometrics, 1:80-83, 1945.
24. G. Zhang, B. Patuwo, and M. Hu. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1):35-62, 1998.