Time series cluster kernel for learning similarities between multivariate time series with missing data

Índice de impacto: Scopus: Q1, T1, P4, Rank 7/189, Computer Vision and Pattern Recognition WoS: Q1, T1, D1, P91, Rank 25/263, Engineering, Electrical and Electronic

Detalles Bibliográficos
Autores: Mikalsen, Karl Øyvind, Bianchi, Filippo Maria, Soguero-Ruiz, Cristina, Jenssen, Robert
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/28208
Acceso en línea:https://hdl.handle.net/10115/28208
Access Level:acceso embargado
Palabra clave:Multivariate time series
Similarity measures
Kernel methods
Missing data
Gaussian mixture models
Ensemble learning
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spelling Time series cluster kernel for learning similarities between multivariate time series with missing dataMikalsen, Karl ØyvindBianchi, Filippo MariaSoguero-Ruiz, CristinaJenssen, RobertMultivariate time seriesSimilarity measuresKernel methodsMissing dataGaussian mixture modelsEnsemble learningÍndice de impacto: Scopus: Q1, T1, P4, Rank 7/189, Computer Vision and Pattern Recognition WoS: Q1, T1, D1, P91, Rank 25/263, Engineering, Electrical and ElectronicSimilarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning, and some have shortcomings if the time series are multivariate (MTS), due to dependencies between attributes, or the time series contain missing data. In this paper, we address these challenges within the powerful context of kernel methods by proposing the robust time series cluster kernel (TCK). The approach taken leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with informative prior distributions. An ensemble learning approach is exploited to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel. We evaluate the TCK on synthetic and real data and compare to other state-of-the-art techniques. The experimental results demonstrate that the TCK is robust to parameter choices, provides competitive results for MTS without missing data and outstanding results for missing data.Elsevier202420242018info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/28208reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/embargoedAccessoai:burjcdigital.urjc.es:10115/282082026-06-24T12:48:17Z
dc.title.none.fl_str_mv Time series cluster kernel for learning similarities between multivariate time series with missing data
title Time series cluster kernel for learning similarities between multivariate time series with missing data
spellingShingle Time series cluster kernel for learning similarities between multivariate time series with missing data
Mikalsen, Karl Øyvind
Multivariate time series
Similarity measures
Kernel methods
Missing data
Gaussian mixture models
Ensemble learning
title_short Time series cluster kernel for learning similarities between multivariate time series with missing data
title_full Time series cluster kernel for learning similarities between multivariate time series with missing data
title_fullStr Time series cluster kernel for learning similarities between multivariate time series with missing data
title_full_unstemmed Time series cluster kernel for learning similarities between multivariate time series with missing data
title_sort Time series cluster kernel for learning similarities between multivariate time series with missing data
dc.creator.none.fl_str_mv Mikalsen, Karl Øyvind
Bianchi, Filippo Maria
Soguero-Ruiz, Cristina
Jenssen, Robert
author Mikalsen, Karl Øyvind
author_facet Mikalsen, Karl Øyvind
Bianchi, Filippo Maria
Soguero-Ruiz, Cristina
Jenssen, Robert
author_role author
author2 Bianchi, Filippo Maria
Soguero-Ruiz, Cristina
Jenssen, Robert
author2_role author
author
author
dc.subject.none.fl_str_mv Multivariate time series
Similarity measures
Kernel methods
Missing data
Gaussian mixture models
Ensemble learning
topic Multivariate time series
Similarity measures
Kernel methods
Missing data
Gaussian mixture models
Ensemble learning
description Índice de impacto: Scopus: Q1, T1, P4, Rank 7/189, Computer Vision and Pattern Recognition WoS: Q1, T1, D1, P91, Rank 25/263, Engineering, Electrical and Electronic
publishDate 2018
dc.date.none.fl_str_mv 2018
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/28208
url https://hdl.handle.net/10115/28208
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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