Information directionality in coupled time series using transcripts.

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.

[1]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[2]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[3]  José Amigó,et al.  Permutation Complexity in Dynamical Systems , 2010 .

[4]  G. Edelman,et al.  Consciousness and Complexity , 1998 .

[5]  L. Glass Synchronization and rhythmic processes in physiology , 2001, Nature.

[6]  M. Paluš,et al.  Inferring the directionality of coupling with conditional mutual information. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Dimitris Kugiumtzis,et al.  Partial transfer entropy on rank vectors , 2013, ArXiv.

[8]  Fraser,et al.  Independent coordinates for strange attractors from mutual information. , 1986, Physical review. A, General physics.

[9]  J. M. Amigó,et al.  Permutation complexity of interacting dynamical systems , 2013, 1305.1735.

[10]  P. G. Larsson,et al.  Reducing the bias of causality measures. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  S. Frenzel,et al.  Partial mutual information for coupling analysis of multivariate time series. , 2007, Physical review letters.

[12]  H Kantz,et al.  Direction of coupling from phases of interacting oscillators: a permutation information approach. , 2008, Physical review letters.

[13]  Edward Ott,et al.  Theoretical mechanics: Crowd synchrony on the Millennium Bridge , 2005, Nature.

[14]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.

[15]  Luca Faes,et al.  Mutual nonlinear prediction as a tool to evaluate coupling strength and directionality in bivariate time series: comparison among different strategies based on k nearest neighbors. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[17]  W. Marsden I and J , 2012 .

[18]  B. Pompe,et al.  Momentary information transfer as a coupling measure of time series. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  C. Granger Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[20]  Wolfram Bunk,et al.  Characterizing synchronization in time series using information measures extracted from symbolic representations. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Matthäus Staniek,et al.  Symbolic transfer entropy. , 2008, Physical review letters.

[22]  Milan Palus,et al.  Direction of coupling from phases of interacting oscillators: an information-theoretic approach. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Wolfram Bunk,et al.  Transcripts: an algebraic approach to coupled time series. , 2012, Chaos.

[24]  M Palus,et al.  Synchronization as adjustment of information rates: detection from bivariate time series. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  R. Burke,et al.  Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[26]  T. Schreiber,et al.  Information transfer in continuous processes , 2002 .