Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/30683
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dc.contributor.authorAMIGO, JOSE M.-
dc.contributor.authorDale, Roberto-
dc.contributor.authorTempesta, Piergiulio-
dc.contributor.otherDepartamentos de la UMH::Estadística, Matemáticas e Informáticaes_ES
dc.date.accessioned2024-01-26T09:54:51Z-
dc.date.available2024-01-26T09:54:51Z-
dc.date.created2021-10-11-
dc.identifier.citationCommunications in Nonlinear Science and Numerical Simulation, 105, (2022), 106077es_ES
dc.identifier.issn1878-7274-
dc.identifier.issn1007-5704-
dc.identifier.urihttps://hdl.handle.net/11000/30683-
dc.description.abstractThis is a paper in the intersection of time series analysis and complexity theory that presents new results on permutation complexity in general and permutation entropy in particular. In this context, permutation complexity refers to the characterization of time series by means of ordinal patterns (permutations), entropic measures, decay rates of missing ordinal patterns, and more. Since the inception of this “ordinal” methodology, its practical application to any type of scalar time series and real-valued processes have proven to be simple and useful. However, the theoretical aspects have remained limited to noiseless deterministic series and dynamical systems, the main obstacle being the super-exponential growth of allowed permutations with length when randomness (also in form of observational noise) is present in the data. To overcome this difficulty, we take a new approach through complexity classes, which are precisely defined by the growth of allowed permutations with length, regardless of the deterministic or noisy nature of the data. We consider three major classes: exponential, sub-factorial and factorial. The next step is to adapt the concept of Z-entropy to each of those classes, which we call permutation entropy because it coincides with the conventional permutation entropy on the exponential class. Z-entropies are a family of group entropies, each of them extensive on a given complexity class. The result is a unified approach to the ordinal analysis of deterministic and random processes, from dynamical systems to white noise, with new concepts and tools. Numerical simulations show that permutation entropy discriminates time series from all complexity classes.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent19es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTime series analysises_ES
dc.subjectDeterministic and random real-valued processeses_ES
dc.subjectMetric and topological permutation entropyes_ES
dc.subjectPermutation complexity classeses_ES
dc.subjectPermutation entropy rate for noisy processeses_ES
dc.subjectDiscrimination of noisy time serieses_ES
dc.subjectNumerical simulationses_ES
dc.subject.classificationLenguajes y sistemas informaticoses_ES
dc.subject.otherCDU::5 - Ciencias puras y naturales::51 - Matemáticases_ES
dc.titleComplexity-based permutation entropies: From deterministic time series to white noisees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.cnsns.2021.106077es_ES
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Artículos Estadística, Matemáticas e Informática


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