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Title: Surrogate generation algorithm for pseudoperiodic time series
Authors: Luo, X
Nakamura, T
Small, M
Issue Date: 2004
Source: Proceedings of International Symposium on Nonlinear Theory and Its Applications (NOLTA’2004), Fukuoka, Japan, 29 November - 3 December, 2004, p. 211-214 How to cite?
Abstract: In this paper we propose an effective surrogate generation algorithm for pseudoperiodic time series, which can properly cope with a large scope of stochastic perturbations in the noisy data sets. As an example of application, we will demonstrate the ability of this algorithm to distinguish chaotic time series from pseudoperiodic ones of the Rössler system. In addition, we will briefly introduce another surrogate generation algorithm for nonlinearity detection whose central idea is similar with that in this algorithm.
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