Constructing ordinal partition transition networks from multivariate time series

Jiayang Zhang, Jie Zhou, Ming Tang, Heng Guo, Michael Small, Yong Zou

    Research output: Contribution to journalArticlepeer-review

    47 Citations (Scopus)


    A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

    Original languageEnglish
    Article number7795
    Number of pages13
    JournalScientific Reports
    Issue number1
    Publication statusPublished - 1 Dec 2017


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