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| A New Method of Detecting Nonlinearity for Time Series Based on Fisher Projection Transform |
| In order to enhance the performance and stability of surrogate data method, we introduce Fisher projection method to combine and project the different nonlinear statistics. In this way, the performance of different statistics can complement each other. In this paper, six kinds of nonlinear chaotic series and linear Gaussian white noise are used to experiment. The results show that the performance of the surrogate data method has been significantly improved. The stability and suitability of the method become better. Specially, comparing with others, the combination of third-order autocovariance, kurtosis and third-order auocorrelation statistics has better performance and suitability. |
| Department of 907, Second Artillery Engineering University, Xi’an 710025, China |
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Abstract In order to enhance the performance and stability of surrogate data method, we introduce Fisher projection method to combine and project the different nonlinear statistics. In this way, the performance of different statistics can complement each other. In this paper, six kinds of nonlinear chaotic series and linear Gaussian white noise are used to experiment. The results show that the performance of the surrogate data method has been significantly improved. The stability and suitability of the method become better. Specially, comparing with others, the combination of third-order autocovariance, kurtosis and third-order auocorrelation statistics has better performance and suitability.
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Received: 02 July 2013
Published: 15 March 2015
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