New predication of chaotic time series based on local Lyapunov exponent

doi:10.1088/1674-1056/22/5/050502

Abstract A new method of predicting chaotic time series is presented based on local Lyapunov exponent, by quantitatively measuring the exponential rate of separation or attraction of two infinitely close trajectories in state space. After reconstructing state space from one-dimensional chaotic time series, neighboring multiple-state vectors of the predicting point are selected to deduce the prediction formula using the definition of local Lyapunov exponent. Numerical simulations are carried out to test its effectiveness and verify its higher precision than two older methods. Effects of number of referential state vectors and added noise on forecasting accuracy are also studied numerically.

Keywords: chaotic time series prediction of chaotic time series local Lyapunov exponent least squares method

Received: 11 June 2012
Revised: 17 September 2012
Published: 01 April 2013

PACS:

05.45.Ac

(Low-dimensional chaos)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61201452).

Corresponding Authors: Zhang Yong
E-mail: ballack-13@163.com

Cite this article:

Zhang Yong New predication of chaotic time series based on local Lyapunov exponent 2013 Chin. Phys. B 22 050502

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