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Using Permutations for Hierarchical Clustering of Time Series

Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to...

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Autores principales: Cánovas, Jose S., Guillamón, Antonio, Ruiz-Abellón, María Carmen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514788/
https://www.ncbi.nlm.nih.gov/pubmed/33267021
http://dx.doi.org/10.3390/e21030306
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author Cánovas, Jose S.
Guillamón, Antonio
Ruiz-Abellón, María Carmen
author_facet Cánovas, Jose S.
Guillamón, Antonio
Ruiz-Abellón, María Carmen
author_sort Cánovas, Jose S.
collection PubMed
description Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to both simulated theoretical and real data series. For simulated time series the distances show good clustering results, both in the case of linear and non-linear dependencies. The effect of the embedding dimension and the linkage method are also analyzed. Finally, several real data series are properly clustered using the proposed method.
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spelling pubmed-75147882020-11-09 Using Permutations for Hierarchical Clustering of Time Series Cánovas, Jose S. Guillamón, Antonio Ruiz-Abellón, María Carmen Entropy (Basel) Article Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time series by dependency. We apply these distances to both simulated theoretical and real data series. For simulated time series the distances show good clustering results, both in the case of linear and non-linear dependencies. The effect of the embedding dimension and the linkage method are also analyzed. Finally, several real data series are properly clustered using the proposed method. MDPI 2019-03-21 /pmc/articles/PMC7514788/ /pubmed/33267021 http://dx.doi.org/10.3390/e21030306 Text en © 2019 by the authors. 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
Cánovas, Jose S.
Guillamón, Antonio
Ruiz-Abellón, María Carmen
Using Permutations for Hierarchical Clustering of Time Series
title Using Permutations for Hierarchical Clustering of Time Series
title_full Using Permutations for Hierarchical Clustering of Time Series
title_fullStr Using Permutations for Hierarchical Clustering of Time Series
title_full_unstemmed Using Permutations for Hierarchical Clustering of Time Series
title_short Using Permutations for Hierarchical Clustering of Time Series
title_sort using permutations for hierarchical clustering of time series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514788/
https://www.ncbi.nlm.nih.gov/pubmed/33267021
http://dx.doi.org/10.3390/e21030306
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