Abstract One of the features of deterministic chaos is sensitive to initial conditions. This feature limits the prediction horizons of many chaotic systems. In this paper, we propose a new prediction technique for chaotic time series. In our method, some neighbouring points of the predicted point, for which the corresponding local Lyapunov exponent is particularly large, would be discarded during estimating the local dynamics, and thus the error accumulated by the prediction algorithm is reduced. The model is tested for the convection amplitude of Lorenz systems. The simulation results indicate that the prediction technique can improve the prediction of chaotic time series.
Received: 04 March 2003
Revised: 16 June 2003
Accepted manuscript online:
Fund: Project supported by the Funds for Outstanding researchers from the National Natural Science Foundation of China (Grant No 70225005), and Research Award Program (2001) for Outstanding Young Teachers in Higher Education Institutions of Ministry of Educatio
Cite this article:
Li Ke-Ping (李克平), Gao Zi-You (高自友), Chen Tian-Lun (陈天仑) Improving the prediction of chaotic time series 2003 Chinese Physics 12 1213
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