Causality and the entropy-complexity plane: Robustness and missing ordinal patterns

We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative weights in a given time series. Two different tools have been advanced in the literature to such effect, namely, (i) the "causal" entropycomplexity plane [O.A. Rosso, H.A. Lar...

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Publicado: 2012
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Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v391_n1-2_p42_Rosso
http://hdl.handle.net/20.500.12110/paper_03784371_v391_n1-2_p42_Rosso
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spelling paper:paper_03784371_v391_n1-2_p42_Rosso2023-06-08T15:40:16Z Causality and the entropy-complexity plane: Robustness and missing ordinal patterns Chaos Entropycomplexity Missing ordinal patterns Noise Time series analysis Chaotic series Colored noise Correlated noise Decay rate Degree of correlations Deterministic component Entropycomplexity Finite time Forbidden pattern Logistic maps Noise Ordinal pattern Random dynamics Relative weights Decay (organic) Time series White noise Time series analysis We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative weights in a given time series. Two different tools have been advanced in the literature to such effect, namely, (i) the "causal" entropycomplexity plane [O.A. Rosso, H.A. Larrondo, M.T. Martín, A. Plastino, M.A. Fuentes, Distinguishing noise from chaos, Phys. Rev. Lett. 99 (2007) 154102] and (ii) the estimation of the decay rate of missing ordinal patterns [J.M. Amigó, S. Zambrano, M.A.F. Sanjuán, True and false forbidden patterns in deterministic and random dynamics, Europhys. Lett. 79 (2007) 50001; L.C. Carpi, P.M. Saco, O.A. Rosso, Missing ordinal patterns in correlated noises. Physica A 389 (2010) 20202029]. In this work we extend the use of these techniques to address the analysis of deterministic finite time series contaminated with additive noises of different degree of correlation. The chaotic series studied here was via the logistic map (r=4) to which we added correlated noise (colored noise with f-k Power Spectrum, 0≤k≤2) of varying amplitudes. In such a fashion important insights pertaining to the deterministic component of the original time series can be gained. We find that in the entropycomplexity plane this goal can be achieved without additional computations. © 2011 Elsevier B.V. All rights reserved. 2012 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v391_n1-2_p42_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v391_n1-2_p42_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 Chaos
Entropycomplexity
Missing ordinal patterns
Noise
Time series analysis
Chaotic series
Colored noise
Correlated noise
Decay rate
Degree of correlations
Deterministic component
Entropycomplexity
Finite time
Forbidden pattern
Logistic maps
Noise
Ordinal pattern
Random dynamics
Relative weights
Decay (organic)
Time series
White noise
Time series analysis
spellingShingle Chaos
Entropycomplexity
Missing ordinal patterns
Noise
Time series analysis
Chaotic series
Colored noise
Correlated noise
Decay rate
Degree of correlations
Deterministic component
Entropycomplexity
Finite time
Forbidden pattern
Logistic maps
Noise
Ordinal pattern
Random dynamics
Relative weights
Decay (organic)
Time series
White noise
Time series analysis
Causality and the entropy-complexity plane: Robustness and missing ordinal patterns
topic_facet Chaos
Entropycomplexity
Missing ordinal patterns
Noise
Time series analysis
Chaotic series
Colored noise
Correlated noise
Decay rate
Degree of correlations
Deterministic component
Entropycomplexity
Finite time
Forbidden pattern
Logistic maps
Noise
Ordinal pattern
Random dynamics
Relative weights
Decay (organic)
Time series
White noise
Time series analysis
description We deal here with the issue of determinism versus randomness in time series. One wishes to identify their relative weights in a given time series. Two different tools have been advanced in the literature to such effect, namely, (i) the "causal" entropycomplexity plane [O.A. Rosso, H.A. Larrondo, M.T. Martín, A. Plastino, M.A. Fuentes, Distinguishing noise from chaos, Phys. Rev. Lett. 99 (2007) 154102] and (ii) the estimation of the decay rate of missing ordinal patterns [J.M. Amigó, S. Zambrano, M.A.F. Sanjuán, True and false forbidden patterns in deterministic and random dynamics, Europhys. Lett. 79 (2007) 50001; L.C. Carpi, P.M. Saco, O.A. Rosso, Missing ordinal patterns in correlated noises. Physica A 389 (2010) 20202029]. In this work we extend the use of these techniques to address the analysis of deterministic finite time series contaminated with additive noises of different degree of correlation. The chaotic series studied here was via the logistic map (r=4) to which we added correlated noise (colored noise with f-k Power Spectrum, 0≤k≤2) of varying amplitudes. In such a fashion important insights pertaining to the deterministic component of the original time series can be gained. We find that in the entropycomplexity plane this goal can be achieved without additional computations. © 2011 Elsevier B.V. All rights reserved.
title Causality and the entropy-complexity plane: Robustness and missing ordinal patterns
title_short Causality and the entropy-complexity plane: Robustness and missing ordinal patterns
title_full Causality and the entropy-complexity plane: Robustness and missing ordinal patterns
title_fullStr Causality and the entropy-complexity plane: Robustness and missing ordinal patterns
title_full_unstemmed Causality and the entropy-complexity plane: Robustness and missing ordinal patterns
title_sort causality and the entropy-complexity plane: robustness and missing ordinal patterns
publishDate 2012
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v391_n1-2_p42_Rosso
http://hdl.handle.net/20.500.12110/paper_03784371_v391_n1-2_p42_Rosso
_version_ 1768544413101850624