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Information directionality in coupled time series using transcripts.

Roberto Monetti1, Wolfram Bunk, Thomas Aschenbrenner

  • 1Max-Planck-Institut für extraterrestrische Physik, Giessenbachstr. 1, 85748 Garching, Germany.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
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Summary
This summary is machine-generated.

This study introduces transcripts in ordinal symbolic dynamics to analyze time series information. The new method reliably assesses coupling directionality in dynamical systems using permutation group structures.

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Area of Science:

  • Symbolic dynamics
  • Information theory
  • Dynamical systems analysis

Background:

  • Ordinal symbolic dynamics uses transcripts to define algebraic relationships between ordinal patterns.
  • Existing methods for assessing information flow in time series can be limited.

Purpose of the Study:

  • To develop novel information measures for time series based on ordinal pattern transcripts.
  • To apply these measures for reliable assessment of coupling directionality in dynamical systems.

Main Methods:

  • Exploiting the mathematical structure of the permutation group.
  • Deriving properties and relations among information measures of symbolic representations.
  • Introducing new coupling directionality measures based solely on transcripts.

Main Results:

  • Theoretical results derived from permutation group structure.
  • New coupling directionality measures improve information flow estimates.
  • Generalization of transcripts unifies several existing directionality measures.

Conclusions:

  • The transcript concept provides a robust framework for analyzing time series information.
  • The proposed measures enhance the reliability of coupling directionality assessment.
  • This approach offers a unified perspective on information directionality measures.