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Network structure of multivariate time series
Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4614448/ https://www.ncbi.nlm.nih.gov/pubmed/26487040 http://dx.doi.org/10.1038/srep15508 |
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author | Lacasa, Lucas Nicosia, Vincenzo Latora, Vito |
author_facet | Lacasa, Lucas Nicosia, Vincenzo Latora, Vito |
author_sort | Lacasa, Lucas |
collection | PubMed |
description | Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. |
format | Online Article Text |
id | pubmed-4614448 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46144482015-10-29 Network structure of multivariate time series Lacasa, Lucas Nicosia, Vincenzo Latora, Vito Sci Rep Article Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail. Nature Publishing Group 2015-10-21 /pmc/articles/PMC4614448/ /pubmed/26487040 http://dx.doi.org/10.1038/srep15508 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Lacasa, Lucas Nicosia, Vincenzo Latora, Vito Network structure of multivariate time series |
title | Network structure of multivariate time series |
title_full | Network structure of multivariate time series |
title_fullStr | Network structure of multivariate time series |
title_full_unstemmed | Network structure of multivariate time series |
title_short | Network structure of multivariate time series |
title_sort | network structure of multivariate time series |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4614448/ https://www.ncbi.nlm.nih.gov/pubmed/26487040 http://dx.doi.org/10.1038/srep15508 |
work_keys_str_mv | AT lacasalucas networkstructureofmultivariatetimeseries AT nicosiavincenzo networkstructureofmultivariatetimeseries AT latoravito networkstructureofmultivariatetimeseries |