Quantifying entropy using recurrence matrix microstates
| dc.contributor.author | Corso, Gilberto | |
| dc.contributor.author | Prado, Thiago de Lima | |
| dc.contributor.author | Lima, Gustavo Zampier dos Santos | |
| dc.contributor.author | Kurths, Jürgen | |
| dc.contributor.author | Lopes, Sérgio Roberto | |
| dc.date.accessioned | 2020-12-04T19:49:32Z | |
| dc.date.available | 2020-12-04T19:49:32Z | |
| dc.date.issued | 2018-08-09 | |
| dc.description.resumo | We conceive a new recurrence quantifier for time series based on the concept of information entropy, in which the probabilities are associated with the presence of microstates defined on the recurrence matrix as small binary submatrices. The new methodology to compute the entropy of a time series has advantages compared to the traditional entropies defined in the literature, namely, a good correlation with the maximum Lyapunov exponent of the system and a weak dependence on the vicinity threshold parameter. Furthermore, the new method works adequately even for small segments of data, bringing consistent results for short and long time series. In a case where long time series are available, the new methodology can be employed to obtain high precision results since it does not demand large computational times related to the analysis of the entire time series or recurrence matrices, as is the case of other traditional entropy quantifiers. The method is applied to discrete and continuous systems | pt_BR |
| dc.identifier.citation | CORSO, Gilberto; PRADO, Thiago de Lima; LIMA, Gustavo Zampier dos Santos; KURTHS, Jürgen; LOPES, Sergio Roberto. Quantifying entropy using recurrence matrix microstates. Chaos: An Interdisciplinary Journal of Nonlinear Science, [S.L.], v. 28, n. 8, p. 083108-083108, ago. 2018. Disponível em: https://aip.scitation.org/doi/10.1063/1.5042026. Acesso em: 20 nov. 2020. http://dx.doi.org/10.1063/1.5042026. | pt_BR |
| dc.identifier.doi | 10.1063/1.5042026 | |
| dc.identifier.issn | 1054-1500 | |
| dc.identifier.issn | 1089-7682 | |
| dc.identifier.uri | https://repositorio.ufrn.br/handle/123456789/30826 | |
| dc.language | pt_BR | pt_BR |
| dc.publisher | American Institute of Physics | pt_BR |
| dc.subject | Lyapunov exponent | pt_BR |
| dc.subject | Logistic map | pt_BR |
| dc.subject | Data visualization | pt_BR |
| dc.subject | Lorenz system | pt_BR |
| dc.subject | Time series analysis | pt_BR |
| dc.subject | Signal processing | pt_BR |
| dc.subject | Phase space methods | pt_BR |
| dc.subject | Entropy | pt_BR |
| dc.title | Quantifying entropy using recurrence matrix microstates | pt_BR |
| dc.type | article | pt_BR |
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