Characterization of time series via Rényi complexity–entropy curves

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity–entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering t...

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Detalles Bibliográficos
Autores principales:	Jauregui, Max, Zunino, Luciano José, Lenzi, Ervin K., Mendes, Renio S., Ribeiro, Haroldo V.
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2018
Materias:	Ciencias Exactas Física Time series R'enyi entropy Complexity measures Ordinal patterns probabilities
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/125460
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-125460
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Exactas Física Time series R'enyi entropy Complexity measures Ordinal patterns probabilities
spellingShingle	Ciencias Exactas Física Time series R'enyi entropy Complexity measures Ordinal patterns probabilities Jauregui, Max Zunino, Luciano José Lenzi, Ervin K. Mendes, Renio S. Ribeiro, Haroldo V. Characterization of time series via Rényi complexity–entropy curves
topic_facet	Ciencias Exactas Física Time series R'enyi entropy Complexity measures Ordinal patterns probabilities
description	One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity–entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity–entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity–entropy curves in the context of the Renyi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Renyi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Renyi complexity–entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Renyi complexity–entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.
format	Articulo Articulo
author	Jauregui, Max Zunino, Luciano José Lenzi, Ervin K. Mendes, Renio S. Ribeiro, Haroldo V.
author_facet	Jauregui, Max Zunino, Luciano José Lenzi, Ervin K. Mendes, Renio S. Ribeiro, Haroldo V.
author_sort	Jauregui, Max
title	Characterization of time series via Rényi complexity–entropy curves
title_short	Characterization of time series via Rényi complexity–entropy curves
title_full	Characterization of time series via Rényi complexity–entropy curves
title_fullStr	Characterization of time series via Rényi complexity–entropy curves
title_full_unstemmed	Characterization of time series via Rényi complexity–entropy curves
title_sort	characterization of time series via rényi complexity–entropy curves
publishDate	2018
url	http://sedici.unlp.edu.ar/handle/10915/125460
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Characterization of time series via Rényi complexity–entropy curves

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