Gdpc: An R package for generalized dynamic principal components

Autores
Peña, Daniel; Smucler, Ezequiel; Yohai, Victor Jaime
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem of dynamical principal components, propose a solution based on a reconstruction criteria and present an automatic procedure to compute the optimal reconstruction. This solution can be applied to the non-stationary case, where the components need not be a linear combination of the observations, as is the case in the proposal of Brillinger (1981). This article discusses some new features that are included in the package and that were not considered in Peña and Yohai (2016). The most important one is an automatic procedure for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. These tools make it easy to use the proposed procedure in large data sets. The procedure can also be used when the number of series is larger than the number of observations. We describe an iterative algorithm and present an example of the use of the package with real data.
Fil: Peña, Daniel. Universidad Carlos III; España
Fil: Smucler, Ezequiel. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yohai, Victor Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina
Materia
DIMENSIONALITY REDUCTION
HIGH-DIMENSIONAL TIME SERIES
R
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/143223
Acceder
id CONICETDig_9d0c256c3acded12015187e51ce2a795
oai_identifier_str oai:ri.conicet.gov.ar:11336/143223
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Gdpc: An R package for generalized dynamic principal componentsPeña, DanielSmucler, EzequielYohai, Victor JaimeDIMENSIONALITY REDUCTIONHIGH-DIMENSIONAL TIME SERIESRhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem of dynamical principal components, propose a solution based on a reconstruction criteria and present an automatic procedure to compute the optimal reconstruction. This solution can be applied to the non-stationary case, where the components need not be a linear combination of the observations, as is the case in the proposal of Brillinger (1981). This article discusses some new features that are included in the package and that were not considered in Peña and Yohai (2016). The most important one is an automatic procedure for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. These tools make it easy to use the proposed procedure in large data sets. The procedure can also be used when the number of series is larger than the number of observations. We describe an iterative algorithm and present an example of the use of the package with real data.Fil: Peña, Daniel. Universidad Carlos III; EspañaFil: Smucler, Ezequiel. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Yohai, Victor Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; ArgentinaJournal Statistical Software2020-02-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/143223Peña, Daniel; Smucler, Ezequiel; Yohai, Victor Jaime; Gdpc: An R package for generalized dynamic principal components; Journal Statistical Software; Journal Of Statistical Software; 92; 23-2-2020; 1-231548-7660CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.18637/jss.v092.c02info:eu-repo/semantics/altIdentifier/url/https://www.jstatsoft.org/article/view/v092c02info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:09:44Zoai:ri.conicet.gov.ar:11336/143223instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:09:44.35CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Gdpc: An R package for generalized dynamic principal components
title Gdpc: An R package for generalized dynamic principal components
spellingShingle Gdpc: An R package for generalized dynamic principal components
Peña, Daniel
DIMENSIONALITY REDUCTION
HIGH-DIMENSIONAL TIME SERIES
R
title_short Gdpc: An R package for generalized dynamic principal components
title_full Gdpc: An R package for generalized dynamic principal components
title_fullStr Gdpc: An R package for generalized dynamic principal components
title_full_unstemmed Gdpc: An R package for generalized dynamic principal components
title_sort Gdpc: An R package for generalized dynamic principal components
dc.creator.none.fl_str_mv Peña, Daniel
Smucler, Ezequiel
Yohai, Victor Jaime
author Peña, Daniel
author_facet Peña, Daniel
Smucler, Ezequiel
Yohai, Victor Jaime
author_role author
author2 Smucler, Ezequiel
Yohai, Victor Jaime
author2_role author
author
dc.subject.none.fl_str_mv DIMENSIONALITY REDUCTION
HIGH-DIMENSIONAL TIME SERIES
R
topic DIMENSIONALITY REDUCTION
HIGH-DIMENSIONAL TIME SERIES
R
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem of dynamical principal components, propose a solution based on a reconstruction criteria and present an automatic procedure to compute the optimal reconstruction. This solution can be applied to the non-stationary case, where the components need not be a linear combination of the observations, as is the case in the proposal of Brillinger (1981). This article discusses some new features that are included in the package and that were not considered in Peña and Yohai (2016). The most important one is an automatic procedure for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. These tools make it easy to use the proposed procedure in large data sets. The procedure can also be used when the number of series is larger than the number of observations. We describe an iterative algorithm and present an example of the use of the package with real data.
Fil: Peña, Daniel. Universidad Carlos III; España
Fil: Smucler, Ezequiel. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Yohai, Victor Jaime. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina
description Gdpc is an R package for the computation of the generalized dynamic principal components proposed in Peña and Yohai (2016). In this paper, we briefly introduce the problem of dynamical principal components, propose a solution based on a reconstruction criteria and present an automatic procedure to compute the optimal reconstruction. This solution can be applied to the non-stationary case, where the components need not be a linear combination of the observations, as is the case in the proposal of Brillinger (1981). This article discusses some new features that are included in the package and that were not considered in Peña and Yohai (2016). The most important one is an automatic procedure for the identification of both the number of lags to be used in the generalized dynamic principal components as well as the number of components required for a given reconstruction accuracy. These tools make it easy to use the proposed procedure in large data sets. The procedure can also be used when the number of series is larger than the number of observations. We describe an iterative algorithm and present an example of the use of the package with real data.
publishDate 2020
dc.date.none.fl_str_mv 2020-02-23
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/143223
Peña, Daniel; Smucler, Ezequiel; Yohai, Victor Jaime; Gdpc: An R package for generalized dynamic principal components; Journal Statistical Software; Journal Of Statistical Software; 92; 23-2-2020; 1-23
1548-7660
CONICET Digital
CONICET
url http://hdl.handle.net/11336/143223
identifier_str_mv Peña, Daniel; Smucler, Ezequiel; Yohai, Victor Jaime; Gdpc: An R package for generalized dynamic principal components; Journal Statistical Software; Journal Of Statistical Software; 92; 23-2-2020; 1-23
1548-7660
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.18637/jss.v092.c02
info:eu-repo/semantics/altIdentifier/url/https://www.jstatsoft.org/article/view/v092c02
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Journal Statistical Software
publisher.none.fl_str_mv Journal Statistical Software
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613979046936576
score 13.070432

Publicaciones similares