Permutation-information-theory approach to unveil delay dynamics from time-series analysis
In this paper an approach to identify delay phenomena from time series is developed. We show that it is possible to perform a reliable time delay identification by using quantifiers derived from information theory, more precisely, permutation entropy and permutation statistical complexity. These qua...
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2010
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15393755_v82_n4_p_Zunino http://hdl.handle.net/20.500.12110/paper_15393755_v82_n4_p_Zunino |
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paper:paper_15393755_v82_n4_p_Zunino2023-06-08T16:20:48Z Permutation-information-theory approach to unveil delay dynamics from time-series analysis Characteristic time Delay dynamics Dynamical noise Feedback rates High nonlinearity Mackey-Glass equations Noise environments Numerical data Permutation approach Permutation entropy Statistical complexity Time delay identification Time-delay systems Equipment testing Harmonic analysis Information theory Time delay Time series Time series analysis In this paper an approach to identify delay phenomena from time series is developed. We show that it is possible to perform a reliable time delay identification by using quantifiers derived from information theory, more precisely, permutation entropy and permutation statistical complexity. These quantifiers show clear extrema when the embedding delay τ of the symbolic reconstruction matches the characteristic time delay τS of the system. Numerical data originating from a time delay system based on the well-known Mackey-Glass equations operating in the chaotic regime were used as test beds. We show that our method is straightforward to apply and robust to additive observational and dynamical noise. Moreover, we find that the identification of the time delay is even more efficient in a noise environment. Our permutation approach is also able to recover the time delay in systems with low feedback rate or high nonlinearity. © 2010 The American Physical Society. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15393755_v82_n4_p_Zunino http://hdl.handle.net/20.500.12110/paper_15393755_v82_n4_p_Zunino |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Characteristic time Delay dynamics Dynamical noise Feedback rates High nonlinearity Mackey-Glass equations Noise environments Numerical data Permutation approach Permutation entropy Statistical complexity Time delay identification Time-delay systems Equipment testing Harmonic analysis Information theory Time delay Time series Time series analysis |
| spellingShingle |
Characteristic time Delay dynamics Dynamical noise Feedback rates High nonlinearity Mackey-Glass equations Noise environments Numerical data Permutation approach Permutation entropy Statistical complexity Time delay identification Time-delay systems Equipment testing Harmonic analysis Information theory Time delay Time series Time series analysis Permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| topic_facet |
Characteristic time Delay dynamics Dynamical noise Feedback rates High nonlinearity Mackey-Glass equations Noise environments Numerical data Permutation approach Permutation entropy Statistical complexity Time delay identification Time-delay systems Equipment testing Harmonic analysis Information theory Time delay Time series Time series analysis |
| description |
In this paper an approach to identify delay phenomena from time series is developed. We show that it is possible to perform a reliable time delay identification by using quantifiers derived from information theory, more precisely, permutation entropy and permutation statistical complexity. These quantifiers show clear extrema when the embedding delay τ of the symbolic reconstruction matches the characteristic time delay τS of the system. Numerical data originating from a time delay system based on the well-known Mackey-Glass equations operating in the chaotic regime were used as test beds. We show that our method is straightforward to apply and robust to additive observational and dynamical noise. Moreover, we find that the identification of the time delay is even more efficient in a noise environment. Our permutation approach is also able to recover the time delay in systems with low feedback rate or high nonlinearity. © 2010 The American Physical Society. |
| title |
Permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| title_short |
Permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| title_full |
Permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| title_fullStr |
Permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| title_full_unstemmed |
Permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| title_sort |
permutation-information-theory approach to unveil delay dynamics from time-series analysis |
| publishDate |
2010 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15393755_v82_n4_p_Zunino http://hdl.handle.net/20.500.12110/paper_15393755_v82_n4_p_Zunino |
| _version_ |
1768542194769068032 |