Testing time series irreversibility using complex network methods

Abstract

The absence of time-reversal symmetry is a fundamental property of many nonlinear time series. Here, we propose a new set of statistical tests for time series irreversibility based on standard and horizontal visibility graphs. Specifically, we statistically compare the distributions of time-directed variants of the common complex network measures degree and local clustering coefficient. Our approach does not involve surrogate data and is applicable to relatively short time series. We demonstrate its performance for paradigmatic model systems with known time-reversal properties as well as for picking up signatures of nonlinearity in neuro-physiological data.

6 pages, 5 figures

References in corpus (4)

• From time series to complex networks: the visibility graph
• Complex Network Approach for Recurrence Analysis of Time Series
• Time series irreversibility: a visibility graph approach
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• Nonequilibrium physics of brain dynamics
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