Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time

Most of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this paper, the capabilities of the Generative Topographic Mapping Through Time, a...

Descripción completa

Detalles Bibliográficos
Autores: Olier, Ivan, Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Tipo de recurso: informe técnico
Fecha de publicación:2005
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/85725
Acceso en línea:https://hdl.handle.net/2117/85725
Access Level:acceso abierto
Palabra clave:Generative topographic mapping
Topology-constrained hidden Markov models
Multivariate time series analysis
Data visualization
Clustering
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
id ES_aa0df1e5da1f7f649d19ea9da5eaf797
oai_identifier_str oai:upcommons.upc.edu:2117/85725
network_acronym_str ES
network_name_str España
repository_id_str
spelling Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through timeOlier, IvanVellido Alcacena, Alfredo|||0000-0002-9843-1911Generative topographic mappingTopology-constrained hidden Markov modelsMultivariate time series analysisData visualizationClusteringÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificialMost of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this paper, the capabilities of the Generative Topographic Mapping Through Time, a model with solid foundations in probability theory that performs simultaneous time series data clustering and visualization, are assessed in detail in several experiments. The focus is placed on the detection of atypical data, the visualization of the evolution of signal regimes, and the exploration of sudden transitions, for which a novel identification index is defined.20052005-11-0120162016-04-15reporthttp://purl.org/coar/resource_type/c_93fcVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/reportapplication/pdfhttps://hdl.handle.net/2117/85725reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/857252026-05-27T15:37:01Z
dc.title.none.fl_str_mv Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
title Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
spellingShingle Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
Olier, Ivan
Generative topographic mapping
Topology-constrained hidden Markov models
Multivariate time series analysis
Data visualization
Clustering
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
title_short Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
title_full Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
title_fullStr Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
title_full_unstemmed Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
title_sort Capturing the dynamics of multivariate time series through visualization using generative topographic mapping through time
dc.creator.none.fl_str_mv Olier, Ivan
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author Olier, Ivan
author_facet Olier, Ivan
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author_role author
author2 Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author2_role author
dc.subject.none.fl_str_mv Generative topographic mapping
Topology-constrained hidden Markov models
Multivariate time series analysis
Data visualization
Clustering
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
topic Generative topographic mapping
Topology-constrained hidden Markov models
Multivariate time series analysis
Data visualization
Clustering
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
description Most of the existing research on time series concerns supervised forecasting problems. In comparison, little research has been devoted to unsupervised methods for the visual exploration of multivariate time series. In this paper, the capabilities of the Generative Topographic Mapping Through Time, a model with solid foundations in probability theory that performs simultaneous time series data clustering and visualization, are assessed in detail in several experiments. The focus is placed on the detection of atypical data, the visualization of the evolution of signal regimes, and the exploration of sudden transitions, for which a novel identification index is defined.
publishDate 2005
dc.date.none.fl_str_mv 2005
2005-11-01
2016
2016-04-15
dc.type.none.fl_str_mv report
http://purl.org/coar/resource_type/c_93fc
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format report
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/85725
url https://hdl.handle.net/2117/85725
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869416111423881216
score 15.301603