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2022-03-18Zeitschriftenartikel DOI: 10.1088/1367-2630/ac5057
A direct method to detect deterministic and stochastic properties of data
dc.contributor.authorPrado, Thiago
dc.contributor.authorBoaretto, Bruno
dc.contributor.authorCorso, Gilberto
dc.contributor.authorDos Santos Lima, Gustavo Zampier
dc.contributor.authorKurths, Jürgen
dc.contributor.authorLopes, Sergio Roberto
dc.date.accessioned2022-06-09T15:18:07Z
dc.date.available2022-06-09T15:18:07Z
dc.date.issued2022-03-18none
dc.date.updated2022-03-26T18:45:32Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/25433
dc.description.abstractAbstractA fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals.eng
dc.description.sponsorshipFinanciadora de Estudos e Projetoshttps://doi.org/10.13039/501100004809
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superiorhttps://doi.org/10.13039/501100002322
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológicohttps://doi.org/10.13039/501100003593
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectstochastic and deterministic signalseng
dc.subjectrecurrence analysiseng
dc.subjectquantifying deterministic and stochastic signalseng
dc.subject.ddc530 Physiknone
dc.titleA direct method to detect deterministic and stochastic properties of datanone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/25433-5
dc.identifier.doi10.1088/1367-2630/ac5057none
dc.identifier.doihttp://dx.doi.org/10.18452/24758
dc.type.versionpublishedVersionnone
local.edoc.container-titleNew journal of physics : the open-access journal for physicsnone
local.edoc.pages19none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionMathematisch-Naturwissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-nameIOPnone
local.edoc.container-publisher-place[London]none
local.edoc.container-volume24none
local.edoc.container-issue3none
dc.description.versionPeer Reviewednone
local.edoc.container-articlenumber033027none
dc.identifier.eissn1367-2630

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