A New Method of Detecting Nonlinear for Time Series Based on KS Test

Hou Shu-min; Li You-rong; Liu Guang-ling

doi:10.3724/SP.J.1146.2005.00998

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Hou Shu-min, Li You-rong, Liu Guang-ling. A New Method of Detecting Nonlinear for Time Series Based on KS Test[J]. Journal of Electronics & Information Technology, 2007, 29(4): 808-810. doi: 10.3724/SP.J.1146.2005.00998

Citation:

Hou Shu-min, Li You-rong, Liu Guang-ling. A New Method of Detecting Nonlinear for Time Series Based on KS Test[J]. Journal of Electronics & Information Technology, 2007, 29(4): 808-810. doi: 10.3724/SP.J.1146.2005.00998

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A New Method of Detecting Nonlinear for Time Series Based on KS Test

doi: 10.3724/SP.J.1146.2005.00998

Received Date: 2005-08-12
Rev Recd Date: 2006-04-17
Publish Date: 2007-04-19

Abstract

Abstract

The choice of test statistics can bring important influence to nonlinear of time series. This paper introduces a Non-parameter testKolmogorov-Smirnov (KS) test into nonlinearity test. After applying the Phase-randomized surrogate algorithm to create surrogate data, three test statistics methods, which include KS test, the third-order autocovariance and the asymmetry due to time reversal, are employed to determine nonlinear of five kinds signals. By comparing the test results, it indicates that KS test is an effective and stable nonlinear test statistics. The proposed method has high noise immunity and more sensitive to nonlinear signal.

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References(1)

References

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