Using robust FPCA to identify outliers in functional time series, with applications to the electricity market

This study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other...

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Detalles Bibliográficos
Autores: Vilar, Juan M.|||0000-0002-5757-5919, Raña, Paula, Aneiros, Germán
Tipo de recurso: artículo
Fecha de publicación:2016
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:168851
Acceso en línea:https://ddd.uab.cat/record/168851
Access Level:acceso abierto
Palabra clave:Functional data analysis
Functional principal component analysis
Functional time series
Outlier detection
Electricity demand and price
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spelling Using robust FPCA to identify outliers in functional time series, with applications to the electricity marketVilar, Juan M.|||0000-0002-5757-5919Raña, PaulaAneiros, GermánFunctional data analysisFunctional principal component analysisFunctional time seriesOutlier detectionElectricity demand and priceThis study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012. 22016-01-0120162016-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/168851reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/3.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:1688512026-06-06T12:50:31Z
dc.title.none.fl_str_mv Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
title Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
spellingShingle Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
Vilar, Juan M.|||0000-0002-5757-5919
Functional data analysis
Functional principal component analysis
Functional time series
Outlier detection
Electricity demand and price
title_short Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
title_full Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
title_fullStr Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
title_full_unstemmed Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
title_sort Using robust FPCA to identify outliers in functional time series, with applications to the electricity market
dc.creator.none.fl_str_mv Vilar, Juan M.|||0000-0002-5757-5919
Raña, Paula
Aneiros, Germán
author Vilar, Juan M.|||0000-0002-5757-5919
author_facet Vilar, Juan M.|||0000-0002-5757-5919
Raña, Paula
Aneiros, Germán
author_role author
author2 Raña, Paula
Aneiros, Germán
author2_role author
author
dc.subject.none.fl_str_mv Functional data analysis
Functional principal component analysis
Functional time series
Outlier detection
Electricity demand and price
topic Functional data analysis
Functional principal component analysis
Functional time series
Outlier detection
Electricity demand and price
description This study proposes two methods for detecting outliers in functional time series. Both methods take dependence in the data into account and are based on robust functional principal component analysis. One method seeks outliers in the series of projections on the first principal component. The other obtains uncontaminated forecasts for each data set and determines that those observations whose residuals have an unusually high norm are considered outliers. A simulation study shows the performance of these proposed procedures and the need to take dependence in the time series into account. Finally, the usefulness of our methodology is illustrated in two real datasets from the electricity market: daily curves of electricity demand and price in mainland Spain, for the year 2012.
publishDate 2016
dc.date.none.fl_str_mv 2
2016-01-01
2016
2016-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/168851
url https://ddd.uab.cat/record/168851
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
https://creativecommons.org/licenses/by-nc-nd/3.0/
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
https://creativecommons.org/licenses/by-nc-nd/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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