Multivariate Chaotic Time Series Prediction Based on Radial Basis Function Neural Network

  • Min Han
  • Wei Guo
  • Mingming Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


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.


Time Series River Runoff Radial Basis Function Neural Network Multivariate Time Series Nonlinear Time Series 


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  1. 1.
    Porporato, A., Ridolfi, L.: Multivariate Nonlinear Prediction of River Flows. Journal of Hydrology 248(1-4), 109–122 (2001)CrossRefGoogle Scholar
  2. 2.
    Suzuki, T., Ikeguchi, T., Suzuki, M.: Multivariable Nonlinear Analysis of Foreign Exchange Rates. Physica A-Statistical Mechanics and Its Applications 323(5), 591–600 (2003)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Tronci, S., Giona, M., Baratti, R.: Reconstruction of Chaotic Time Series by Neural Models: A Case Study. Neurocomputing 55(3-4), 581–591 (2003)CrossRefGoogle Scholar
  4. 4.
    Han, M., Xi, J., Xu, S., Yin, F.L.: Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network. IEEE Transaction on Signal Processing 52(12), 3409–3416 (2004)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Barron, A.: Approximation and Estimation Bounds for Artificial Neural Network, Technical Report 59, Department of Statistics, University of Illinois at Urbana-Champaign (1991)Google Scholar
  6. 6.
    Rosenstein, M.T., Collins, J.J., De Luca, C.J.: Reconstruction Expansion as a Geometry-Based Framework for Choosing Proper Delay Time. Physica D: Nonlinear Phenomena 73(1-4), 82–98 (1994)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Kim, H.S., Eykholt, R., Salas, J.D.: Nonlinear Dynamics, Delay Times, and Embedding Windows. Physica D: Nonlinear Phenomena 127(1-2), 48–60 (1999)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min Han
    • 1
  • Wei Guo
    • 1
  • Mingming Fan
    • 1
  1. 1.School of Electronic and Information EngineeringDalian University of TechnologyDalianChina

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