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Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is exami...

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Autor principal: Papana, Angeliki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700128/
https://www.ncbi.nlm.nih.gov/pubmed/34945876
http://dx.doi.org/10.3390/e23121570
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author Papana, Angeliki
author_facet Papana, Angeliki
author_sort Papana, Angeliki
collection PubMed
description The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.
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spelling pubmed-87001282021-12-24 Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality Papana, Angeliki Entropy (Basel) Article The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance. MDPI 2021-11-25 /pmc/articles/PMC8700128/ /pubmed/34945876 http://dx.doi.org/10.3390/e23121570 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Papana, Angeliki
Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
title Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
title_full Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
title_fullStr Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
title_full_unstemmed Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
title_short Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality
title_sort connectivity analysis for multivariate time series: correlation vs. causality
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700128/
https://www.ncbi.nlm.nih.gov/pubmed/34945876
http://dx.doi.org/10.3390/e23121570
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