Abstract
In this paper, a new predictive algorithm for multivariate chaotic time series is proposed. Considering the correlations among time series, multivariate time series instead of univariate ones are taken as the inputs of predictive model. The model is implemented by a radial basis function neural network. To determine the number of model inputs, C-C method is applied to construct the embedding of the chaotic time series by choosing delay time window. The annual river runoff and annual sunspots are used in the simulation, and the proposed method is proven effective and valid.
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© 2006 Springer-Verlag Berlin Heidelberg
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Han, M., Guo, W., Fan, M. (2006). Multivariate Chaotic Time Series Prediction Based on Radial Basis Function Neural Network. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_109
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DOI: https://doi.org/10.1007/11760023_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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