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Open AccessArticle

Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series

1
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, University Campus, Thessaloniki 54124, Greece
2
Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki 57001, Greece
3
Department of Computer, Informatics and Telecommunications Engineering, International Hellenic University, Serres 62124, Greece
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(11), 1080; https://doi.org/10.3390/e21111080
Received: 15 September 2019 / Revised: 23 October 2019 / Accepted: 31 October 2019 / Published: 4 November 2019
(This article belongs to the Special Issue Complex Networks from Information Measures)
Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. These measures are in the time domain, such as model-based and information measures, the frequency domain, and the phase domain. The study aims also to compare bivariate and multivariate measures, linear and nonlinear measures, as well as the use of dimension reduction in linear model-based measures and information measures. The latter is particular relevant in the study of high-dimensional time series. For the performance of the multivariate causality measures, low and high dimensional coupled dynamical systems are considered in discrete and continuous time, as well as deterministic and stochastic. The measures are evaluated and ranked according to their ability to provide causality networks that match the original coupling structure. The simulation study concludes that the Granger causality measures using dimension reduction are superior and should be preferred particularly in studies involving many observed variables, such as multi-channel electroencephalograms and financial markets. View Full-Text
Keywords: Granger causality; causality networks; dimension reduction measures; multivariate time series Granger causality; causality networks; dimension reduction measures; multivariate time series
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Siggiridou, E.; Koutlis, C.; Tsimpiris, A.; Kugiumtzis, D. Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series. Entropy 2019, 21, 1080.

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