Characterizing time series via complexity-entropy curves

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propos...

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Autores principales: Ribeiro, Haroldo V., Jauregui, Max, Zunino, Luciano José, Lenzi, Ervin K.
Formato: Articulo Preprint
Lenguaje:Inglés
Publicado: 2017
Materias:
Física
Fractal dimension
Chaotic
Applied mathematics
Mathematics
Probability and statistics
Complex system
Entropy (information theory)
System dynamics
Stochastic process
Parametric equation
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/124818
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-124818
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Fractal dimension
Chaotic
Applied mathematics
Mathematics
Probability and statistics
Complex system
Entropy (information theory)
System dynamics
Stochastic process
Parametric equation
spellingShingle Física
Fractal dimension
Chaotic
Applied mathematics
Mathematics
Probability and statistics
Complex system
Entropy (information theory)
System dynamics
Stochastic process
Parametric equation
Ribeiro, Haroldo V.
Jauregui, Max
Zunino, Luciano José
Lenzi, Ervin K.
Characterizing time series via complexity-entropy curves
topic_facet Física
Fractal dimension
Chaotic
Applied mathematics
Mathematics
Probability and statistics
Complex system
Entropy (information theory)
System dynamics
Stochastic process
Parametric equation
description The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q-complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.
format Articulo
Preprint
author Ribeiro, Haroldo V.
Jauregui, Max
Zunino, Luciano José
Lenzi, Ervin K.
author_facet Ribeiro, Haroldo V.
Jauregui, Max
Zunino, Luciano José
Lenzi, Ervin K.
author_sort Ribeiro, Haroldo V.
title Characterizing time series via complexity-entropy curves
title_short Characterizing time series via complexity-entropy curves
title_full Characterizing time series via complexity-entropy curves
title_fullStr Characterizing time series via complexity-entropy curves
title_full_unstemmed Characterizing time series via complexity-entropy curves
title_sort characterizing time series via complexity-entropy curves
publishDate 2017
url http://sedici.unlp.edu.ar/handle/10915/124818
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AT jaureguimax characterizingtimeseriesviacomplexityentropycurves
AT zuninolucianojose characterizingtimeseriesviacomplexityentropycurves
AT lenziervink characterizingtimeseriesviacomplexityentropycurves
bdutipo_str Repositorios
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