Logo do repositório
 
Publicação

Computational performance analysis of PCA enhanced AnEn method

2025Comunicação em conferência Acesso restrito
Ver registo simples
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Engenharia do Ambiente
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg13:Ação Climática
dc.contributor.authorBreve, Murilo Montanini
dc.contributor.authorCamargos, Ana Flavia P.
dc.contributor.authorRufino, José
dc.contributor.authorBalsa, Carlos
dc.date.accessioned2026-02-13T14:28:10Z
dc.date.available2026-02-13T14:28:10Z
dc.date.issued2025
dc.description.abstractThe Analog Ensemble (AnEn) method allows to reconstruct incomplete time series, based on correlated series with full records. It has been extensively applied to meteorological data, which may involve many variables and stations, and span many years, slowing down the reconstruction. To accelerate this process, Principal Component Analysis (PCA) may be employed, to combine several input series into a few principal components (PCs), over which AnEn is then applied for a faster data reconstruction. The integration of PCA with K-means clustering further amplifies efficiency. In this paper, multicore-based implementations of non-PCA and PCA-based methods, in MATLAB, R, and Python, were scrutinized to ascertain numerical consistency and evaluate relative performance. The experiments show that when using PCA, accuracy is kept, or even improved, despite lowering the number of input time series. Performance-wise, the experiments revealed a distinct edge for the Python code, for which the benefits of parallel processing were most evident. Preliminary results are also shown for a Python variant that exploits GPUs for the analog search, with very promising speedups.eng
dc.description.sponsorshipThis work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).
dc.identifier.citationBreve, Murilo Montanini; Camargos, Ana Flavia P.; Rufino, José; Balsa, Carlos (2025). Computational performance analysis of PCA enhanced AnEn method. In Breve, Murilo Montanini; Camargos, Ana Flavia P.; Rufino, José; Balsa, Carlos (2025). Computational performance analysis of PCA enhanced AnEn method. In ARTIIS 2024 International Workshops. Chile. p. 18-33. ISBN 978-3-031-83435-6
dc.identifier.doi10.1007/978-3-031-83435-6_2
dc.identifier.isbn978-3-031-83434-9
dc.identifier.isbn978-3-031-83435-6
dc.identifier.urihttp://hdl.handle.net/10198/35745
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofAdvanced Research in Technologies, Information, Innovation and Sustainability
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAnalog ensemble
dc.subjectCUDA
dc.subjectK-means clustering
dc.subjectMATLAB
dc.subjectMeteorological records reconstruction
dc.subjectPCA
dc.subjectPython
dc.subjectR
dc.titleComputational performance analysis of PCA enhanced AnEn methodpor
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.endPage33
oaire.citation.startPage18
oaire.citation.titleBreve, Murilo Montanini; Camargos, Ana Flavia P.; Rufino, José; Balsa, Carlos (2025). Computational Performance Analysis of PCA Enhanced AnEn Method. In ARTIIS 2024 International Workshops. Cham: Springer Nature. Part I, p. 18-33. ISBN 978-3-031-83435-6
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRufino
person.familyNameBalsa
person.givenNameJosé
person.givenNameCarlos
person.identifier1721518
person.identifier.ciencia-idC414-F47F-6323
person.identifier.ciencia-idDE1E-2F7A-AAB1
person.identifier.orcid0000-0002-1344-8264
person.identifier.orcid0000-0003-2431-8665
person.identifier.ridM-8735-2013
person.identifier.scopus-author-id55947199100
person.identifier.scopus-author-id23391719100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication1e24d2ce-a354-442a-bef8-eebadd94b385
relation.isAuthorOfPublicationd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isAuthorOfPublication.latestForDiscoveryd0e5ccff-9696-4f4f-9567-8d698a6bf17d
relation.isProjectOfPublication6e01ddc8-6a82-4131-bca6-84789fa234bd
relation.isProjectOfPublicationd0a17270-80a8-4985-9644-a04c2a9f2dff
relation.isProjectOfPublication6255046e-bc79-4b82-8884-8b52074b4384
relation.isProjectOfPublication.latestForDiscovery6e01ddc8-6a82-4131-bca6-84789fa234bd

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
Computational_Performance_Analysis.pdf
Tamanho:
1.11 MB
Formato:
Adobe Portable Document Format
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.75 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus