Assessing Time Series Reversibility through Permutation Patterns
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 predi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/9/665 |
| _version_ | 1811213483421728768 |
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| author | Massimiliano Zanin Alejandro Rodríguez-González Ernestina Menasalvas Ruiz David Papo |
| author_facet | Massimiliano Zanin Alejandro Rodríguez-González Ernestina Menasalvas Ruiz David Papo |
| author_sort | Massimiliano Zanin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T05:46:03Z |
| format | Article |
| id | doaj.art-381a47e230124507a68f2aa7bde414cb |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-12T05:46:03Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-381a47e230124507a68f2aa7bde414cb2022-12-22T03:45:26ZengMDPI AGEntropy1099-43002018-09-0120966510.3390/e20090665e20090665Assessing Time Series Reversibility through Permutation PatternsMassimiliano Zanin0Alejandro Rodríguez-González1Ernestina Menasalvas Ruiz2David Papo3Center for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, 28040 Madrid, SpainCenter for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, 28040 Madrid, SpainCenter for Biomedical Technology, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, 28040 Madrid, SpainSCALab UMR CNRS 9193, University of Lille, 59800 Villeneuve d’Ascq, FranceTime 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.http://www.mdpi.com/1099-4300/20/9/665time irreversibilitypermutation entropyvisibility graphsefficient market hypothesis |
| spellingShingle | Massimiliano Zanin Alejandro Rodríguez-González Ernestina Menasalvas Ruiz David Papo Assessing Time Series Reversibility through Permutation Patterns Entropy time irreversibility permutation entropy visibility graphs efficient market hypothesis |
| title | Assessing Time Series Reversibility through Permutation Patterns |
| title_full | Assessing Time Series Reversibility through Permutation Patterns |
| title_fullStr | Assessing Time Series Reversibility through Permutation Patterns |
| title_full_unstemmed | Assessing Time Series Reversibility through Permutation Patterns |
| title_short | Assessing Time Series Reversibility through Permutation Patterns |
| title_sort | assessing time series reversibility through permutation patterns |
| topic | time irreversibility permutation entropy visibility graphs efficient market hypothesis |
| url | http://www.mdpi.com/1099-4300/20/9/665 |
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