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Correlation Dimension Detects Causal Links in Coupled Dynamical Systems
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 dete...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515347/ http://dx.doi.org/10.3390/e21090818 |
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author | Krakovská, Anna |
author_facet | Krakovská, Anna |
author_sort | Krakovská, Anna |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7515347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75153472020-11-09 Correlation Dimension Detects Causal Links in Coupled Dynamical Systems Krakovská, Anna Entropy (Basel) Article 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. MDPI 2019-08-21 /pmc/articles/PMC7515347/ http://dx.doi.org/10.3390/e21090818 Text en © 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Krakovská, Anna Correlation Dimension Detects Causal Links in Coupled Dynamical Systems |
title | Correlation Dimension Detects Causal Links in Coupled Dynamical Systems |
title_full | Correlation Dimension Detects Causal Links in Coupled Dynamical Systems |
title_fullStr | Correlation Dimension Detects Causal Links in Coupled Dynamical Systems |
title_full_unstemmed | Correlation Dimension Detects Causal Links in Coupled Dynamical Systems |
title_short | Correlation Dimension Detects Causal Links in Coupled Dynamical Systems |
title_sort | correlation dimension detects causal links in coupled dynamical systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515347/ http://dx.doi.org/10.3390/e21090818 |
work_keys_str_mv | AT krakovskaanna correlationdimensiondetectscausallinksincoupleddynamicalsystems |