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Optimal Latent Variables Number for the Reconstruction of Time Series with PLSR

2024Conference paper Restricted access
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dc.contributor.authorBalsa, Carlos
dc.contributor.authorDupuis, Hugo
dc.contributor.authorBreve, Murilo Montanini
dc.contributor.authorGuivarch, Ronan
dc.contributor.authorRufino, José
dc.date.accessioned2025-02-12T16:55:09Z
dc.date.available2025-02-12T16:55:09Z
dc.date.issued2024
dc.description.abstractThe Partial Least Squares Regression is an efficient method for the filling of gaps in meteorological time series. It enables to reduce the dimension of the predictor dataset to a reduced number of latent variables, without loss of significant information. Defining the number of latent variables to be used is an essential aspect of the success of the method. This study is about the comparison between eight different criteria, used in the choice of latent variables. The results indicate that the criteria based on cross-validation are the most efficient, being, however, more computationally demanding.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBalsa, Carlos; Dupuis, Hugo; Breve, Murilo Montanini; Guivarch, Ronan; Rufino, José (2024). Optimal Latent Variables Number for the Reconstruction of Time Series with PLSR. In International Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023). Cham: Springer Nature, p. 193-205. ISBN 978-3-031-69227-7.pt_PT
dc.identifier.doi10.1007/978-3-031-69228-4_13pt_PT
dc.identifier.isbn978-3-031-69227-7
dc.identifier.urihttp://hdl.handle.net/10198/31185
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPartial least squares regressionpt_PT
dc.subjectLatent variablespt_PT
dc.subjectTime seriespt_PT
dc.subjectCross validationpt_PT
dc.titleOptimal Latent Variables Number for the Reconstruction of Time Series with PLSRpt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage205pt_PT
oaire.citation.startPage193pt_PT
oaire.citation.titleInternational Conference on Computer Science, Electronics and Industrial Engineering (CSEI 2023)pt_PT
person.familyNameBalsa
person.familyNameRufino
person.givenNameCarlos
person.givenNameJosé
person.identifier1721518
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.ciencia-idC414-F47F-6323
person.identifier.orcid0000-0003-2431-8665
person.identifier.orcid0000-0002-1344-8264
person.identifier.ridM-8735-2013
person.identifier.scopus-author-id23391719100
person.identifier.scopus-author-id55947199100
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isAuthorOfPublication1e24d2ce-a354-442a-bef8-eebadd94b385
relation.isAuthorOfPublication.latestForDiscoveryd0e5ccff-9696-4f4f-9567-8d698a6bf17d

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