Literature DB >> 24032905

Information directionality in coupled time series using transcripts.

Roberto Monetti1, Wolfram Bunk, Thomas Aschenbrenner, Stephan Springer, José M Amigó.   

Abstract

In ordinal symbolic dynamics, transcripts describe the algebraic relationship between ordinal patterns. Using the concept of transcript, we exploit the mathematical structure of the group of permutations to derive properties and relations among information measures of the symbolic representations of time series. These theoretical results are then applied for the assessment of coupling directionality in dynamical systems, where suitable coupling directionality measures are introduced depending only on transcripts. These measures improve the reliability of the information flow estimates and reduce to well-established coupling directionality quantifiers when some general conditions are satisfied. Furthermore, by generalizing the definition of transcript to ordinal patterns of different lengths, several of the commonly used information directionality measures can be encompassed within the same framework.

Year:  2013        PMID: 24032905     DOI: 10.1103/PhysRevE.88.022911

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


  3 in total

1.  Infragranular layers lead information flow during slow oscillations according to information directionality indicators.

Authors:  J M Amigó; R Monetti; N Tort-Colet; M V Sanchez-Vives
Journal:  J Comput Neurosci       Date:  2015-05-14       Impact factor: 1.621

Review 2.  Ordinal symbolic analysis and its application to biomedical recordings.

Authors:  José M Amigó; Karsten Keller; Valentina A Unakafova
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2015-02-13       Impact factor: 4.226

3.  Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples.

Authors:  Yoshito Hirata; José M Amigó; Yoshiya Matsuzaka; Ryo Yokota; Hajime Mushiake; Kazuyuki Aihara
Journal:  PLoS One       Date:  2016-07-05       Impact factor: 3.240

  3 in total

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