Causal Inference in Time Series in Terms of Renyi Transfer Entropy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F22%3A00360003" target="_blank" >RIV/68407700:21340/22:00360003 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/e24070855" target="_blank" >https://doi.org/10.3390/e24070855</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/e24070855" target="_blank" >10.3390/e24070855</a>

Result language

angličtina

Original language name

Causal Inference in Time Series in Terms of Renyi Transfer Entropy

Original language description

Uncovering causal interdependencies from observational data is one of the great challenges of a nonlinear time series analysis. In this paper, we discuss this topic with the help of an information-theoretic concept known as Renyi's information measure. In particular, we tackle the directional information flow between bivariate time series in terms of Renyi's transfer entropy. We show that by choosing Renyi's parameter alpha, we can appropriately control information that is transferred only between selected parts of the underlying distributions. This, in turn, is a particularly potent tool for quantifying causal interdependencies in time series, where the knowledge of "black swan" events, such as spikes or sudden jumps, are of key importance. In this connection, we first prove that for Gaussian variables, Granger causality and Renyi transfer entropy are entirely equivalent. Moreover, we also partially extend these results to heavy-tailed alpha-Gaussian variables. These results allow establishing a connection between autoregressive and Renyi entropy-based information-theoretic approaches to data-driven causal inference. To aid our intuition, we employed the Leonenko et al. entropy estimator and analyzed Renyi's information flow between bivariate time series generated from two unidirectionally coupled Rossler systems. Notably, we find that Renyi's transfer entropy not only allows us to detect a threshold of synchronization but it also provides non-trivial insight into the structure of a transient regime that exists between the region of chaotic correlations and synchronization threshold. In addition, from Renyi's transfer entropy, we could reliably infer the direction of coupling and, hence, causality, only for coupling strengths smaller than the onset value of the transient regime, i.e., when two Rossler systems are coupled but have not yet entered synchronization.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/GA19-16066S" target="_blank" >GA19-16066S: Nonlinear interactions and information transfer in complex systems with extreme events</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Entropy

ISSN

1099-4300

e-ISSN

1099-4300

Volume of the periodical

24

Issue of the periodical within the volume

7

Country of publishing house

CH - SWITZERLAND

Number of pages

32

Pages from-to

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UT code for WoS article

000833648700001

EID of the result in the Scopus database

2-s2.0-85133212785