Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score

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• dc.contributor.author Naro, Daniel
• dc.contributor.author Rummel, Christian
• dc.contributor.author Schindler, Kaspar A.
• dc.contributor.author Andrzejak, Ralph Gregor
• dc.date.accessioned 2020-02-18T14:52:14Z
• dc.date.available 2020-02-18T14:52:14Z
• dc.date.issued 2014
• dc.description.abstract The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).en
• dc.description.sponsorship R.G.A. acknowledges Grant No. FIS-2010-18204 of the Spanish Ministry of Education and Science and funding from the Volkswagen Foundation. K.S. and C.R. are grateful for support by the Swiss National Science Foundation (Projects No. SNF 320030-122010 and No. 33CM30-124089).
• dc.format.mimetype application/pdf
• dc.identifier.citation Naro D, Rummel C, Schindler K, Andrzejak RG. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score. Phys Rev E. 2014;90(3):032913. DOI: 10.1103/PhysRevE.90.032913
• dc.identifier.doi http://dx.doi.org/10.1103/PhysRevE.90.032913
• dc.identifier.issn 2470-0045
• dc.identifier.uri http://hdl.handle.net/10230/43638
• dc.language.iso eng
• dc.publisher American Physical Society
• dc.relation.ispartof Physical Review E. 2014;90(3):032913
• dc.relation.isreferencedby http://hdl.handle.net/10230/42940
• dc.relation.projectID info:eu-repo/grant/Agreement/ES/3PN/FIS2010-18204
• dc.rights © American Physical Society. Published article available at https://journals.aps.org/pre/pdf/10.1103/PhysRevE.90.032913
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Nonlinear signal analysisen
• dc.subject.keyword Determinismen
• dc.subject.keyword Electroencephalographic recordingsen
• dc.subject.keyword Epilepsyen
• dc.title Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability scoreen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion