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Article

Correlation Dimension Detects Causal Links in Coupled Dynamical Systems

Institute of Measurement Science, Slovak Academy of Sciences, 841 04 Bratislava, Slovakia
Entropy 2019, 21(9), 818; https://doi.org/10.3390/e21090818
Received: 17 June 2019 / Revised: 16 August 2019 / Accepted: 20 August 2019 / Published: 21 August 2019
It is becoming increasingly clear that causal analysis of dynamical systems requires different approaches than, for example, causal analysis of interconnected autoregressive processes. In this study, a correlation dimension estimated in reconstructed state spaces is used to detect causality. If deterministic dynamics plays a dominant role in data then the method based on the correlation dimension can serve as a fast and reliable way to reveal causal relationships between and within the systems. This study demonstrates that the method, unlike most other causal approaches, detects causality well, even for very weak links. It can also identify cases of uncoupled systems that are causally affected by a hidden common driver. View Full-Text
Keywords: causality; correlation dimension; common driver; the arrow of time causality; correlation dimension; common driver; the arrow of time
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MDPI and ACS Style

Krakovská, A. Correlation Dimension Detects Causal Links in Coupled Dynamical Systems. Entropy 2019, 21, 818. https://doi.org/10.3390/e21090818

AMA Style

Krakovská A. Correlation Dimension Detects Causal Links in Coupled Dynamical Systems. Entropy. 2019; 21(9):818. https://doi.org/10.3390/e21090818

Chicago/Turabian Style

Krakovská, Anna. 2019. "Correlation Dimension Detects Causal Links in Coupled Dynamical Systems" Entropy 21, no. 9: 818. https://doi.org/10.3390/e21090818

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