Detecting dynamical changes in time series by using the Jensen Shannon divergence
Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá.
| Main Authors: | , , |
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| Other Authors: | |
| Format: | info:eu-repo/semantics/publishedVersion |
| Language: | eng |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://hdl.handle.net/11086/553789 https://dx.doi.org/10.1063/1.4999613 |
| _version_ | 1825131964397518848 |
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| author | Mateos, Diego Martín Riveaud, Leonardo Esteban Lamberti, Pedro Walter |
| author2 | https://orcid.org/0000-0002-1953-0875 |
| author_facet | https://orcid.org/0000-0002-1953-0875 Mateos, Diego Martín Riveaud, Leonardo Esteban Lamberti, Pedro Walter |
| author_sort | Mateos, Diego Martín |
| collection | Repositorio Digital Universitario |
| description | Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá. |
| format | info:eu-repo/semantics/publishedVersion |
| id | rdu-unc.553789 |
| institution | Universidad Nacional de Cordoba |
| language | eng |
| publishDate | 2024 |
| record_format | dspace |
| spelling | rdu-unc.5537892024-09-25T16:05:24Z Detecting dynamical changes in time series by using the Jensen Shannon divergence Mateos, Diego Martín Riveaud, Leonardo Esteban Lamberti, Pedro Walter https://orcid.org/0000-0002-1953-0875 Chaos Noise Jensen Shannon divergence info:eu-repo/semantics/publishedVersion Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá. Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series. http://aip.scitation.org/doi/10.1063/1.4999613 info:eu-repo/semantics/publishedVersion Fil: Mateos, Diego Martín. University of Toronto. Hospital for Sick Children; Canadá. Fil: Riveaud, Leonardo Esteban. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Riveaud, Leonardo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Lamberti, Pedro Walter. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía, Física y Computación; Argentina. Fil: Lamberti, Pedro Walter. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Otras Ciencias Físicas 2024-09-25T14:15:12Z 2024-09-25T14:15:12Z 2017 article Mateos, D. M., Riveaud, L. E. y Lamberti, P. W. (2017). Detecting dynamical changes in time series by using the Jensen Shannon divergence. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27 (8), 083118. https://dx.doi.org/10.1063/1.4999613 1054-1500 http://hdl.handle.net/11086/553789 1089-7682 https://dx.doi.org/10.1063/1.4999613 eng Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es Impreso; Electrónico y/o Digital |
| spellingShingle | Chaos Noise Jensen Shannon divergence Mateos, Diego Martín Riveaud, Leonardo Esteban Lamberti, Pedro Walter Detecting dynamical changes in time series by using the Jensen Shannon divergence |
| title | Detecting dynamical changes in time series by using the Jensen Shannon divergence |
| title_full | Detecting dynamical changes in time series by using the Jensen Shannon divergence |
| title_fullStr | Detecting dynamical changes in time series by using the Jensen Shannon divergence |
| title_full_unstemmed | Detecting dynamical changes in time series by using the Jensen Shannon divergence |
| title_short | Detecting dynamical changes in time series by using the Jensen Shannon divergence |
| title_sort | detecting dynamical changes in time series by using the jensen shannon divergence |
| topic | Chaos Noise Jensen Shannon divergence |
| url | http://hdl.handle.net/11086/553789 https://dx.doi.org/10.1063/1.4999613 |
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