Improved surrogate data for nonlinearity tests

Details Improved surrogate data for nonlinearity tests Improved surrogate data for nonlinearity tests

1999-09-30

arxiv.org/abs/chao-dyn/9909041v1

Phys. Rev. Lett. 77 (1996) 635

Nonlinear Sciences

Chaotic Dynamics

4 pages, 4 figures

Scientific paper

10.1103/PhysRevLett.77.635

Current tests for nonlinearity compare a time series to the null hypothesis of a Gaussian linear stochastic process. For this restricted null assumption, random surrogates can be constructed which are constrained by the linear properties of the data. We propose a more general null hypothesis allowing for nonlinear rescalings of a Gaussian linear process. We show that such rescalings cannot be accounted for by a simple amplitude adjustment of the surrogates which leads to spurious detection of nonlinearity. An iterative algorithm is proposed to make appropriate surrogates which have the same autocorrelations as the data and the same probability distribution.

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