Distinguishing noise from chaos

Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent proba...

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Detalles Bibliográficos
Publicado: 2007
Materias:
Chaotic systems
Computational complexity
Entropy
Random processes
Time series analysis
Bandt-Pompe recipe
Chaotic nature
Complexity-entropy causality plane
Pertinent probability distribution
Acoustic noise
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00319007_v99_n15_p_Rosso
http://hdl.handle.net/20.500.12110/paper_00319007_v99_n15_p_Rosso
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_00319007_v99_n15_p_Rosso
record_format dspace
spelling paper:paper_00319007_v99_n15_p_Rosso2023-06-08T14:58:46Z Distinguishing noise from chaos Chaotic systems Computational complexity Entropy Random processes Time series analysis Bandt-Pompe recipe Chaotic nature Complexity-entropy causality plane Pertinent probability distribution Acoustic noise Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise. © 2007 The American Physical Society. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00319007_v99_n15_p_Rosso http://hdl.handle.net/20.500.12110/paper_00319007_v99_n15_p_Rosso
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Chaotic systems
Computational complexity
Entropy
Random processes
Time series analysis
Bandt-Pompe recipe
Chaotic nature
Complexity-entropy causality plane
Pertinent probability distribution
Acoustic noise
spellingShingle Chaotic systems
Computational complexity
Entropy
Random processes
Time series analysis
Bandt-Pompe recipe
Chaotic nature
Complexity-entropy causality plane
Pertinent probability distribution
Acoustic noise
Distinguishing noise from chaos
topic_facet Chaotic systems
Computational complexity
Entropy
Random processes
Time series analysis
Bandt-Pompe recipe
Chaotic nature
Complexity-entropy causality plane
Pertinent probability distribution
Acoustic noise
description Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise. © 2007 The American Physical Society.
title Distinguishing noise from chaos
title_short Distinguishing noise from chaos
title_full Distinguishing noise from chaos
title_fullStr Distinguishing noise from chaos
title_full_unstemmed Distinguishing noise from chaos
title_sort distinguishing noise from chaos
publishDate 2007
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00319007_v99_n15_p_Rosso
http://hdl.handle.net/20.500.12110/paper_00319007_v99_n15_p_Rosso
_version_ 1768541789043556352

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