Abstract
We propose an efficient hybrid neural network for chaotic time series prediction. The hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropagation and a local model, which is implemented as a time delay embedding. The feed-forward network performs as the global approximation and the local model works as the local approximation. Experimental results using Mackey-Glass data and K.U. Leuven competition data show that the proposed method can predict the more long term than each of predictors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brockwell, P. J. and Davis, R. A.: Introduction to Time Series and Forecasting. Springer-Verlag, New York (1996)
Lapedes, A. and Farber, R.: Nonlinear signal processing using neural networks: Prediction and system modelling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos (1987)
Casdagli, M.: Nonlinear Prediction of Chaotic Time Series. Physica D 35 (1989) 335–356
Weigend, A. S. and Gershenfeld, N. A. (eds.): Time Series Prediction: Forecasting the Future and Understanding the Past. Santa Fe Institute Studies in the Sciences of Complexity, Vol. 15. Perseus Books Publishing, Reading (1994)
Haykin, S.: Neural Networks: A comprehensive foundation. 2nd edn. Prentice-Hall, Upper Saddle River (1999)
Casdagli, M., Sauer, T. and Yorke, J.: Embedology. Journal of Statistical Physics 65 (1991) 579–616
Mackey, M. C. and Glass, L.: Oscillation and Chaos in Physiologist Control Systems. Science 197 (1977) 287–289
Suykens, J. and Vandewalle, J. (eds.): Nonlinear Modeling: Advanced Black-box Techniques. Kluwer Academic Publishers, Boston (1998)
Takens, F.: Detecting strange attractors in turbulence. In: Rand, D. and Young, L. (eds.): Dynamical Systems and Turbulence, Warwick 1980. Lecture Notes in Mathematics, Vol. 898. Springer-Verlag, Berlin (1981) 366–381
Rumelhart, D. E., Hinton, G. E. and Williams, R. J.: Learning Internal Representations by Error Propagation. In: Rumelhart, D. E., McClelland, J. L. and the PDP Research Group (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition. The MIT Press, Cambridge (1986) 318–362
McNames, J., Suykens, J. and Vandewalle, J.: Winning Entry of the K. U. Leuven Time Series Prediction Competition. International Journal of Bifurcation and Chaos 9 (1999) 1485–1500
Suykens, J. and Vandewalle, J.: The K.U. Leuven Competition Data: a Challenge for Advanced Neural Network Techniques. In: European Symposium on Artificial Neural Networks, ENNS, D-Facto public, Bruges (2000) 299–304
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Inoue, H., Fukunaga, Y., Narihisa, H. (2001). Efficient Hybrid Neural Network for Chaotic Time Series Prediction. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_99
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_99
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive