Time series regression modelling: replication, estimation and aggregation through maximum entropy

Detalhes bibliográficos
Autor(a) principal: Duarte, Jorge
Data de Publicação: 2023
Outros Autores: Costa, Maria, Macedo, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/39490
Resumo: In today's world of large volumes of data, where the usual statistical estimation methods are commonly inefficient or, more often, impossible to use, aggregation methodologies have emerged as a solution for statistical inference. This work proposes a novel procedure for time series regression modelling, in which maximum entropy and information theory play central roles in the replication of time series, estimation of parameters, and aggregation of estimates. The preliminary results reveal that this three-stage maximum entropy approach is a promising procedure for time series regression modelling in big data contexts.
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spelling Time series regression modelling: replication, estimation and aggregation through maximum entropyBig dataBootstrapMaximum entropyNeaggingRegression modellingTime seriesIn today's world of large volumes of data, where the usual statistical estimation methods are commonly inefficient or, more often, impossible to use, aggregation methodologies have emerged as a solution for statistical inference. This work proposes a novel procedure for time series regression modelling, in which maximum entropy and information theory play central roles in the replication of time series, estimation of parameters, and aggregation of estimates. The preliminary results reveal that this three-stage maximum entropy approach is a promising procedure for time series regression modelling in big data contexts.MDPI2023-10-12T10:32:24Z2023-07-03T00:00:00Z2023-07-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/39490eng10.3390/engproc2023039039Duarte, JorgeCosta, MariaMacedo, Pedroinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:17:07Zoai:ria.ua.pt:10773/39490Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:09:39.232444Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Time series regression modelling: replication, estimation and aggregation through maximum entropy
title Time series regression modelling: replication, estimation and aggregation through maximum entropy
spellingShingle Time series regression modelling: replication, estimation and aggregation through maximum entropy
Duarte, Jorge
Big data
Bootstrap
Maximum entropy
Neagging
Regression modelling
Time series
title_short Time series regression modelling: replication, estimation and aggregation through maximum entropy
title_full Time series regression modelling: replication, estimation and aggregation through maximum entropy
title_fullStr Time series regression modelling: replication, estimation and aggregation through maximum entropy
title_full_unstemmed Time series regression modelling: replication, estimation and aggregation through maximum entropy
title_sort Time series regression modelling: replication, estimation and aggregation through maximum entropy
author Duarte, Jorge
author_facet Duarte, Jorge
Costa, Maria
Macedo, Pedro
author_role author
author2 Costa, Maria
Macedo, Pedro
author2_role author
author
dc.contributor.author.fl_str_mv Duarte, Jorge
Costa, Maria
Macedo, Pedro
dc.subject.por.fl_str_mv Big data
Bootstrap
Maximum entropy
Neagging
Regression modelling
Time series
topic Big data
Bootstrap
Maximum entropy
Neagging
Regression modelling
Time series
description In today's world of large volumes of data, where the usual statistical estimation methods are commonly inefficient or, more often, impossible to use, aggregation methodologies have emerged as a solution for statistical inference. This work proposes a novel procedure for time series regression modelling, in which maximum entropy and information theory play central roles in the replication of time series, estimation of parameters, and aggregation of estimates. The preliminary results reveal that this three-stage maximum entropy approach is a promising procedure for time series regression modelling in big data contexts.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-12T10:32:24Z
2023-07-03T00:00:00Z
2023-07-03
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/39490
url http://hdl.handle.net/10773/39490
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3390/engproc2023039039
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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