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Clustering time series based on dependence structure
The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods ap...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231620/ https://www.ncbi.nlm.nih.gov/pubmed/30418982 http://dx.doi.org/10.1371/journal.pone.0206753 |
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author | Zhang, Beibei An, Baiguo |
author_facet | Zhang, Beibei An, Baiguo |
author_sort | Zhang, Beibei |
collection | PubMed |
description | The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods applicable to time series with a general and dependent structure. We propose a copula-based distance to measure dissimilarity among time series and consider an estimator for it, where the strong consistency of the estimator is guaranteed. Once the pairwise distance matrix for time series has been obtained, we apply a hierarchical clustering algorithm to cluster the time series and ensure its consistency. Numerical studies, including a large number of simulations and analysis of practical data, show that our method performs well. |
format | Online Article Text |
id | pubmed-6231620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-62316202018-11-19 Clustering time series based on dependence structure Zhang, Beibei An, Baiguo PLoS One Research Article The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation, in this paper, we study clustering methods applicable to time series with a general and dependent structure. We propose a copula-based distance to measure dissimilarity among time series and consider an estimator for it, where the strong consistency of the estimator is guaranteed. Once the pairwise distance matrix for time series has been obtained, we apply a hierarchical clustering algorithm to cluster the time series and ensure its consistency. Numerical studies, including a large number of simulations and analysis of practical data, show that our method performs well. Public Library of Science 2018-11-12 /pmc/articles/PMC6231620/ /pubmed/30418982 http://dx.doi.org/10.1371/journal.pone.0206753 Text en © 2018 Zhang, An http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhang, Beibei An, Baiguo Clustering time series based on dependence structure |
title | Clustering time series based on dependence structure |
title_full | Clustering time series based on dependence structure |
title_fullStr | Clustering time series based on dependence structure |
title_full_unstemmed | Clustering time series based on dependence structure |
title_short | Clustering time series based on dependence structure |
title_sort | clustering time series based on dependence structure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6231620/ https://www.ncbi.nlm.nih.gov/pubmed/30418982 http://dx.doi.org/10.1371/journal.pone.0206753 |
work_keys_str_mv | AT zhangbeibei clusteringtimeseriesbasedondependencestructure AT anbaiguo clusteringtimeseriesbasedondependencestructure |