Principal dynamical components

A new procedure is proposed for the dimensional reduction of time series. Similarly to principal components, the procedure seeks a low-dimensional manifold that minimizes information loss. Unlike principal components, however, the new procedure involves dynamical considerations, through the proposal...

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Detalhes bibliográficos
Autores: Domínguez de la Iglesia, Manuel, Tabak, Esteban G.
Formato: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2013
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/42398
Acesso em linha:http://hdl.handle.net/11441/42398
https://doi.org/10.1002/cpa.21411
Access Level:acceso abierto
Palavra-chave:Principal component analysis
Time series
Empirical orthogonal functions
Autocorrelation
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spelling Principal dynamical componentsDomínguez de la Iglesia, ManuelTabak, Esteban G.Principal component analysisTime seriesEmpirical orthogonal functionsAutocorrelationA new procedure is proposed for the dimensional reduction of time series. Similarly to principal components, the procedure seeks a low-dimensional manifold that minimizes information loss. Unlike principal components, however, the new procedure involves dynamical considerations, through the proposal of a predictive dynamical model in the reduced manifold. Hence the minimization of the uncertainty is not only over the choice of a reduced manifold, as in principal components, but also over the parameters of the dynamical model. Further generalizations are provided to non-autonomous and nonMarkovian scenarios, which are then applied to historical sea-surface temperature data.Dirección General de Enseñanza SuperiorJunta de AndalucíaMinisterio de Ciencia e InnovaciónNational Science Foundation (United States)WileyAnálisis Matemático2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/11441/42398https://doi.org/10.1002/cpa.21411reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésCommunications on Pure and Applied Mathematics, 66 (1), 48-82.BFM2006-13000-C03-01FQM-229FQM-481P06-FQM-017382008-0207DMS 0908077http://dx.doi.org/10.1002/cpa.21411Hoboken (New Jersey)info:eu-repo/semantics/openAccessoai:idus.us.es:11441/423982026-06-17T12:51:07Z
dc.title.none.fl_str_mv Principal dynamical components
title Principal dynamical components
spellingShingle Principal dynamical components
Domínguez de la Iglesia, Manuel
Principal component analysis
Time series
Empirical orthogonal functions
Autocorrelation
title_short Principal dynamical components
title_full Principal dynamical components
title_fullStr Principal dynamical components
title_full_unstemmed Principal dynamical components
title_sort Principal dynamical components
dc.creator.none.fl_str_mv Domínguez de la Iglesia, Manuel
Tabak, Esteban G.
author Domínguez de la Iglesia, Manuel
author_facet Domínguez de la Iglesia, Manuel
Tabak, Esteban G.
author_role author
author2 Tabak, Esteban G.
author2_role author
dc.contributor.none.fl_str_mv Análisis Matemático
dc.subject.none.fl_str_mv Principal component analysis
Time series
Empirical orthogonal functions
Autocorrelation
topic Principal component analysis
Time series
Empirical orthogonal functions
Autocorrelation
description A new procedure is proposed for the dimensional reduction of time series. Similarly to principal components, the procedure seeks a low-dimensional manifold that minimizes information loss. Unlike principal components, however, the new procedure involves dynamical considerations, through the proposal of a predictive dynamical model in the reduced manifold. Hence the minimization of the uncertainty is not only over the choice of a reduced manifold, as in principal components, but also over the parameters of the dynamical model. Further generalizations are provided to non-autonomous and nonMarkovian scenarios, which are then applied to historical sea-surface temperature data.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11441/42398
https://doi.org/10.1002/cpa.21411
url http://hdl.handle.net/11441/42398
https://doi.org/10.1002/cpa.21411
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Communications on Pure and Applied Mathematics, 66 (1), 48-82.
BFM2006-13000-C03-01
FQM-229
FQM-481
P06-FQM-01738
2008-0207
DMS 0908077
http://dx.doi.org/10.1002/cpa.21411
Hoboken (New Jersey)
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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