Causal coupling inference from multivariate time series based on ordinal partition transition networks

Narayan Puthanmadam Subramaniyam, Reik V. Donner, Davide Caron, Gabriella Panuccio, Jari Hyttinen

    Tutkimustuotos: ArtikkeliTieteellinenvertaisarvioitu

    14 Sitaatiot (Scopus)
    33 Lataukset (Pure)

    Abstrakti

    Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.

    AlkuperäiskieliEnglanti
    JulkaisuNonlinear Dynamics
    Vuosikerta105
    Numero1
    DOI - pysyväislinkit
    TilaJulkaistu - 2021
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

    Julkaisufoorumi-taso

    • Jufo-taso 2

    !!ASJC Scopus subject areas

    • Control and Systems Engineering
    • Aerospace Engineering
    • Ocean Engineering
    • Mechanical Engineering
    • Electrical and Electronic Engineering
    • Applied Mathematics

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