Zanin, M.; Rodríguez-González, A.; Ruiz, E.M.; Papo, D. Assessing Time Series Reversibility through Permutation Patterns. Entropy2018, 20, 665.
Zanin, M.; Rodríguez-González, A.; Ruiz, E.M.; Papo, D. Assessing Time Series Reversibility through Permutation Patterns. Entropy 2018, 20, 665.
Zanin, M.; Rodríguez-González, A.; Ruiz, E.M.; Papo, D. Assessing Time Series Reversibility through Permutation Patterns. Entropy2018, 20, 665.
Zanin, M.; Rodríguez-González, A.; Ruiz, E.M.; Papo, D. Assessing Time Series Reversibility through Permutation Patterns. Entropy 2018, 20, 665.
Abstract
Time irreversibility, i.e. the lack of invariance of the statistical properties of a system under time reversal, is a fundamental property of all systems operating out of equilibrium. Time reversal symmetry is associated with important statistical and physical properties and is related to the predictability of the system generating the time series. Over the past fifteen years, various methods to quantify time irreversibility in time series have been proposed, but these can be computationally expensive. Here we propose a new method, based on permutation entropy, which is essentially parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence properties. We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of our method with respect to a recently proposed method based on visibility graphs, and discuss the implications of our results for financial data analysis and interpretation.
Keywords
time irreversibility; permutation entropy; visibility graphs; efficient market hypothesis
Subject
PHYSICAL SCIENCES, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
