Causality Distance Measures for Multivariate Time Series with Applications

In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances togeth...

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Autores principales:	Achilleas Anastasiou, Peter Hatzopoulos, Alex Karagrigoriou, George Mavridoglou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	multivariate time series Granger causality clustering classification distance divergence Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/fb23504d23f24182bcff01db1ad0188b
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spelling	oai:doaj.org-article:fb23504d23f24182bcff01db1ad0188b2021-11-11T18:16:04ZCausality Distance Measures for Multivariate Time Series with Applications10.3390/math92127082227-7390https://doaj.org/article/fb23504d23f24182bcff01db1ad0188b2021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2708https://doaj.org/toc/2227-7390In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.Achilleas AnastasiouPeter HatzopoulosAlex KaragrigoriouGeorge MavridoglouMDPI AGarticlemultivariate time seriesGranger causalityclusteringclassificationdistancedivergenceMathematicsQA1-939ENMathematics, Vol 9, Iss 2708, p 2708 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multivariate time series Granger causality clustering classification distance divergence Mathematics QA1-939
spellingShingle	multivariate time series Granger causality clustering classification distance divergence Mathematics QA1-939 Achilleas Anastasiou Peter Hatzopoulos Alex Karagrigoriou George Mavridoglou Causality Distance Measures for Multivariate Time Series with Applications
description	In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.
format	article
author	Achilleas Anastasiou Peter Hatzopoulos Alex Karagrigoriou George Mavridoglou
author_facet	Achilleas Anastasiou Peter Hatzopoulos Alex Karagrigoriou George Mavridoglou
author_sort	Achilleas Anastasiou
title	Causality Distance Measures for Multivariate Time Series with Applications
title_short	Causality Distance Measures for Multivariate Time Series with Applications
title_full	Causality Distance Measures for Multivariate Time Series with Applications
title_fullStr	Causality Distance Measures for Multivariate Time Series with Applications
title_full_unstemmed	Causality Distance Measures for Multivariate Time Series with Applications
title_sort	causality distance measures for multivariate time series with applications
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fb23504d23f24182bcff01db1ad0188b
work_keys_str_mv	AT achilleasanastasiou causalitydistancemeasuresformultivariatetimeserieswithapplications AT peterhatzopoulos causalitydistancemeasuresformultivariatetimeserieswithapplications AT alexkaragrigoriou causalitydistancemeasuresformultivariatetimeserieswithapplications AT georgemavridoglou causalitydistancemeasuresformultivariatetimeserieswithapplications
_version_	1718431920137699328

Causality Distance Measures for Multivariate Time Series with Applications

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