On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

Murari, Andrea; Lungaroni, Michele; Peluso, Emmanuele; Gaudio, Pasquale; Lerche, Ernesto; Garzotti, Luca; Gelfusa, Michela; JET Contributors,

doi:10.3390/e20090627

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On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

¹,

^2,*

^3,4,

³,

and JET Contributors^†

Consorzio RFX (CNR, ENEA, INFN, Universita’ di Padova, Acciaierie Venete SpA), I-35127 Padova, Italy

Associazione EUROfusion—University of Rome “Tor Vergata”, Via Orazio Raimondo, 18, 00173 Roma, Italy

EUROfusion Programme Management Unit, JET, Culham Science Centre, Abingdon OX14 3DB, UK

⁴

LPP-ERM/KMS, Association EUROFUSION-Belgian State, TEC partner, Brussels 1000, Belgium

Author to whom correspondence should be addressed.

^†

See the author list of “X. Litaudon et al., 2017 Nucl. Fusion 57 102001.

Entropy 2018, 20(9), 627; https://doi.org/10.3390/e20090627

Received: 28 June 2018 / Revised: 14 August 2018 / Accepted: 15 August 2018 / Published: 22 August 2018

(This article belongs to the Special Issue Entropy: From Physics to Information Sciences and Geometry)

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Abstract

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach. View Full-Text

Keywords: transfer entropy; mutual information; Pearson correlation coefficient; time series; causality detection; sawteeth; pacing; ELMs; pellets

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MDPI and ACS Style

Murari, A.; Lungaroni, M.; Peluso, E.; Gaudio, P.; Lerche, E.; Garzotti, L.; Gelfusa, M.; JET Contributors. On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals. Entropy 2018, 20, 627. https://doi.org/10.3390/e20090627

AMA Style

Murari A, Lungaroni M, Peluso E, Gaudio P, Lerche E, Garzotti L, Gelfusa M, JET Contributors. On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals. Entropy. 2018; 20(9):627. https://doi.org/10.3390/e20090627

Chicago/Turabian Style

Murari, Andrea, Michele Lungaroni, Emmanuele Peluso, Pasquale Gaudio, Ernesto Lerche, Luca Garzotti, Michela Gelfusa, and JET Contributors. 2018. "On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals" Entropy 20, no. 9: 627. https://doi.org/10.3390/e20090627

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