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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Detection of time reversibility in time series by ordinal patterns analysis

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Author(s):
Martinez, J. H. [1] ; Herrera-Diestra, J. L. [2] ; Chavez, M. [3]
Total Authors: 3
Affiliation:
[1] Sorbonne Univ, INSERM, UM1127, Inst Cerveau & Moelle Epiniere, F-75013 Paris - France
[2] Univ Estadual Paulista, ICTP South Amer Inst Fundamental Res, IFT, BR-01140070 Sao Paulo - Brazil
[3] Hop La Pitie Salpetriere, CNRS, UMR7225, F-75013 Paris - France
Total Affiliations: 3
Document type: Journal article
Source: Chaos; v. 28, n. 12 DEC 2018.
Web of Science Citations: 6
Abstract

Time irreversibility is a common signature of nonlinear processes and a fundamental property of non-equilibrium systems driven by non-conservative forces. A time series is said to be reversible if its statistical properties are invariant regardless of the direction of time. Here, we propose the Time Reversibility from Ordinal Patterns method (TiROP) to assess time-reversibility from an observed finite time series. TiROP captures the information of scalar observations in time forward as well as its time-reversed counterpart by means of ordinal patterns. The method compares both underlying information contents by quantifying its (dis)-similarity via the Jensen-Shannon divergence. The statistic is contrasted with a population of divergences coming from a set of surrogates to unveil the temporal nature and its involved time scales. We tested TiROP in different synthetic and real, linear, and non-linear time series, juxtaposed with results from the classical Ramsey's time reversibility test. Our results depict a novel, fast-computation, and fully data-driven methodology to assess time-reversibility with no further assumptions over data. This approach adds new insights into the current non-linear analysis techniques and also could shed light on determining new physiological biomarkers of high reliability and computational efficiency. Published by AIP Publishing. (AU)

FAPESP's process: 17/00344-2 - Approaching complex networks for time series analysis
Grantee:Jose Luis Herrera Diestra
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 16/01343-7 - ICTP South American Institute for Fundamental Research: a regional center for theoretical physics
Grantee:Nathan Jacob Berkovits
Support Opportunities: Special Projects