Institute of Information Theory and Automation

Publication details

Causality in Time Series: Its Detection and Quantification by Means of Information Theory

Monography Chapter

Hlaváčková-Schindler Kateřina


serial: Information Theory and Statistical Learning, p. 183-207 , Eds: Emmert-Streib Frank, Dehmer Matthias

research: CEZ:AV0Z10750506

project(s): 2C06001, GA MŠk

keywords: causality, time series, information theory

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abstract (eng):

While studying complex systems, one of the fundamental questions is to identify causal relationships (i.e., which system drives which) between relevant subsystems. In this paper, we focus on information-theoretic approaches for causality detection by means of directionality index based on mutual information estimation. We briefly review the current methods for mutual information estimation from the point of view of their consistency. We also present some arguments from recent literature, supporting the usefulness of the information-theoretic tools for causality detection.

abstract (cze):

Nalezení příčinných vztahů je důležitým krokem při studiu složitých systémů. Práce řeší tento problém z hlediska teorie informace.

RIV: BD

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Last modification: 21.12.2012
Institute of Information Theory and Automation