Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting

The time irreversibility of a dynamical process refers to the phenomenon where its behaviour or statistical properties change when it is observed under a time-reversal operation, i.e., backwards in time and indicates the presence of an “arrow of time”. It is an important feature of both synthetic an...

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Autores: Zanin, Massimiliano, Trajanovski, Pece, Jolakoski, Petar, Sandev, Trifce, Kocarev, Ljupco
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/370820
Acceso en línea:http://hdl.handle.net/10261/370820
Access Level:acceso abierto
Palabra clave:Time irreversibility
Statistical test
Geometric Brownian motion
Time series
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spelling Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic ResettingZanin, MassimilianoTrajanovski, PeceJolakoski, PetarSandev, TrifceKocarev, LjupcoTime irreversibilityStatistical testGeometric Brownian motionTime seriesThe time irreversibility of a dynamical process refers to the phenomenon where its behaviour or statistical properties change when it is observed under a time-reversal operation, i.e., backwards in time and indicates the presence of an “arrow of time”. It is an important feature of both synthetic and real-world systems, as it represents a macroscopic property that describes the mechanisms driving the dynamics at a microscale level and that stems from non-linearities and the presence of non-conservative forces within them. While many alternatives have been proposed in recent decades to assess this feature in experimental time series, the evaluation of their performance is hindered by the lack of benchmark time series of known reversibility. To solve this problem, we here propose and evaluate the use of a geometric Brownian motion model with stochastic resetting. We specifically use synthetic time series generated with this model to evaluate eight irreversibility tests in terms of sensitivity with respect to several characteristics, including their degree of irreversibility and length. We show how tests yield at times contradictory results, including the false detection of irreversible dynamics in time-reversible systems with a frequency higher than expected by chance and how most of them detect a multi-scale irreversibility structure that is not present in the underlying data.Grant CNS2023-144775 funded by MICIU/AEI/10.13039/501100011033 by “European Union NextGenerationEU/PRTR”. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 851255). This work has been partially supported by the María de Maeztu project CEX2021-001164-M funded by the MCIN/AEI/10.13039/501100011033. P.T., P.J., T.S. and L.K. acknowledge financial support by the German Science Foundation (DFG, Grant number ME 1535/12-1) and by the Alliance of International Science Organizations (Project No. ANSO-CR-PP-2022-05).With funding from the Spanish government through the ‘María de Maeztu Unit of Excelence’ accreditation (CEX2021-001164-M).Peer reviewedMultidisciplinary Digital Publishing InstituteEuropean CommissionEuropean Research CouncilAgencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2024202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/370820reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/851255info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001164-MThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.3390/sym16111445https://doi.org/10.3390/sym16111445Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3708202026-05-22T06:33:51Z
dc.title.none.fl_str_mv Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
title Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
spellingShingle Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
Zanin, Massimiliano
Time irreversibility
Statistical test
Geometric Brownian motion
Time series
title_short Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
title_full Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
title_fullStr Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
title_full_unstemmed Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
title_sort Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic Resetting
dc.creator.none.fl_str_mv Zanin, Massimiliano
Trajanovski, Pece
Jolakoski, Petar
Sandev, Trifce
Kocarev, Ljupco
author Zanin, Massimiliano
author_facet Zanin, Massimiliano
Trajanovski, Pece
Jolakoski, Petar
Sandev, Trifce
Kocarev, Ljupco
author_role author
author2 Trajanovski, Pece
Jolakoski, Petar
Sandev, Trifce
Kocarev, Ljupco
author2_role author
author
author
author
dc.contributor.none.fl_str_mv European Commission
European Research Council
Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Time irreversibility
Statistical test
Geometric Brownian motion
Time series
topic Time irreversibility
Statistical test
Geometric Brownian motion
Time series
description The time irreversibility of a dynamical process refers to the phenomenon where its behaviour or statistical properties change when it is observed under a time-reversal operation, i.e., backwards in time and indicates the presence of an “arrow of time”. It is an important feature of both synthetic and real-world systems, as it represents a macroscopic property that describes the mechanisms driving the dynamics at a microscale level and that stems from non-linearities and the presence of non-conservative forces within them. While many alternatives have been proposed in recent decades to assess this feature in experimental time series, the evaluation of their performance is hindered by the lack of benchmark time series of known reversibility. To solve this problem, we here propose and evaluate the use of a geometric Brownian motion model with stochastic resetting. We specifically use synthetic time series generated with this model to evaluate eight irreversibility tests in terms of sensitivity with respect to several characteristics, including their degree of irreversibility and length. We show how tests yield at times contradictory results, including the false detection of irreversible dynamics in time-reversible systems with a frequency higher than expected by chance and how most of them detect a multi-scale irreversibility structure that is not present in the underlying data.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/370820
url http://hdl.handle.net/10261/370820
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/851255
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/CEX2021-001164-M
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.3390/sym16111445
https://doi.org/10.3390/sym16111445
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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