Literature DB >> 17025523

Testing for nonlinearity in irregular fluctuations with long-term trends.

Tomomichi Nakamura1, Michael Small, Yoshito Hirata.   

Abstract

We describe a method for investigating nonlinearity in irregular fluctuations (short-term variability) of time series even if the data exhibit long-term trends (periodicities). Such situations are theoretically incompatible with the assumption of previously proposed methods. The null hypothesis addressed by our algorithm is that irregular fluctuations are generated by a stationary linear system. The method is demonstrated for numerical data generated by known systems and applied to several actual time series.

Year:  2006        PMID: 17025523     DOI: 10.1103/PhysRevE.74.026205

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  7 in total

1.  Dynamic complexity measures and entropy paths for modelling and comparison of evolution of patients with drug resistant epileptic encephalopathy syndromes (DREES).

Authors:  Ricardo Zavala-Yoe; Ricardo A Ramirez-Mendoza
Journal:  Metab Brain Dis       Date:  2017-06-09       Impact factor: 3.584

2.  A comprehensive study of the delay vector variance method for quantification of nonlinearity in dynamical systems.

Authors:  V Jaksic; D P Mandic; K Ryan; B Basu; V Pakrashi
Journal:  R Soc Open Sci       Date:  2016-01-06       Impact factor: 2.963

3.  The meaning of "coherent" and its quantification in coherent hemodynamics spectroscopy.

Authors:  Angelo Sassaroli; Kristen Tgavalekos; Sergio Fantini
Journal:  J Innov Opt Health Sci       Date:  2018-09-27

4.  Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate.

Authors:  Aikaterini Vraka; Fernando Hornero; Vicente Bertomeu-González; Joaquín Osca; Raúl Alcaraz; José J Rieta
Journal:  Entropy (Basel)       Date:  2020-02-19       Impact factor: 2.524

5.  Surrogate Data Preserving All the Properties of Ordinal Patterns up to a Certain Length.

Authors:  Yoshito Hirata; Masanori Shiro; José M Amigó
Journal:  Entropy (Basel)       Date:  2019-07-22       Impact factor: 2.524

6.  Inferring correlations associated to causal interactions in brain signals using autoregressive models.

Authors:  Víctor J López-Madrona; Fernanda S Matias; Claudio R Mirasso; Santiago Canals; Ernesto Pereda
Journal:  Sci Rep       Date:  2019-11-19       Impact factor: 4.379

7.  Dyconnmap: Dynamic connectome mapping-A neuroimaging python module.

Authors:  Avraam D Marimpis; Stavros I Dimitriadis; Rainer Goebel
Journal:  Hum Brain Mapp       Date:  2021-07-11       Impact factor: 5.038

  7 in total

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