Abstract
The prediction of chaotic time series based on the phase space reconstruction was studied in this paper. Considering the exponent divergence of the trajectories of chaotic attractors, the nearest neighbours prediction model was improved with instant Lyapunov exponent, and the evaluation index which was applied to characterize the accuracy of prediction was given. Then, the influence of chaotic attractor structure on the prediction was investigated. Results show that when the phase space reconstruction parameters are chosen improperly, the attractor is deformed clearly, and the prediction errors increase obviously. Furthermore, the prediction is affected more seriously by the delay time than embedding dimension, but the calculation complexity is increased with higher embedding parameter.
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© 2011 Springer-Verlag Berlin Heidelberg
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Liu, S., Zhang, Y., Zhu, S., He, Q. (2011). Study on Predication of Chaotic Time Series Based on Phase Space Reconstruction. In: Jiang, L. (eds) Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE2011) November 19–20, 2011, Melbourne, Australia. Advances in Intelligent and Soft Computing, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25194-8_11
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DOI: https://doi.org/10.1007/978-3-642-25194-8_11
Publisher Name: Springer, Berlin, Heidelberg
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