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    Network structure of multivariate time series. 
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    Network structure of multivariate time series.

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    Published version (1.109Mb)
    Volume
    5
    Pagination
    15508 - ?
    DOI
    10.1038/srep15508
    Journal
    Sci Rep
    Metadata
    Show full item record
    Abstract
    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.
    Authors
    Lacasa, L; Nicosia, V; Latora, V
    URI
    http://qmro.qmul.ac.uk/xmlui/handle/123456789/18369
    Collections
    • Applied Mathematics [140]
    Language
    eng
    Licence information
    CC-BY
    Copyright statements
    © 2015, Rights Managed by Nature Publishing Group
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