The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series

Página do item simplificado
dc.creatorLeonardo Luiz Portes dos Santos
dc.creatorArthur Noronha Montanari
dc.creatorDébora Corrêa
dc.creatorMichael Small
dc.creatorLuis Antonio Aguirre
dc.date.accessioned2025-05-21T14:24:32Z
dc.date.accessioned2025-09-09T01:12:27Z
dc.date.available2025-05-21T14:24:32Z
dc.date.issued2019
dc.identifier.doihttps://doi.org/10.1063/1.5093197
dc.identifier.issn1054-1500
dc.identifier.urihttps://hdl.handle.net/1843/82415
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofChaos
dc.rightsAcesso Restrito
dc.subjectSistemas não lineares
dc.subject.otherLyapunov exponent
dc.subject.otherNonlinear systems
dc.subject.otherDynamical systems
dc.subject.otherChaotic regime
dc.subject.otherNetwork analysis
dc.subject.otherTime series analysis
dc.titleThe reliability of recurrence network analysis is influenced by the observability properties of the recorded time series
dc.typeArtigo de periódico
local.citation.issue8
local.citation.spage083101
local.citation.volume29
local.description.resumoRecurrence network analysis (RNA) is a remarkable technique for the detection of dynamical transitions in experimental applications. However, in practical experiments, often only a scalar time series is recorded. This requires the state-space reconstruction from this single time series which, as established by embedding and observability theory, is shown to be hampered if the recorded variable conveys poor observability. In this work, we investigate how RNA metrics are impacted by the observability properties of the recorded time series. Following the framework of Zou et al. [Chaos 20, 043130 (2010)], we use the Rössler and Duffing-Ueda systems as benchmark models for our study. It is shown that usually RNA metrics perform badly with variables of poor observability as for recurrence quantification analysis. An exception is the clustering coefficient, which is rather robust to observability issues. Along with its efficacy to detect dynamical transitions, it is shown to be an efficient tool for RNA-especially when no prior information of the variable observability is available.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://pubs.aip.org/aip/cha/article/29/8/083101/1058576/The-reliability-of-recurrence-network-analysis-is

Arquivos

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG