Combining a regression model with a multivariate Markov chain in a forecasting problem

Detalhes bibliográficos
Autor(a) principal: Damásio, Bruno
Data de Publicação: 2014
Outros Autores: Nicolau, João
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/27570
Resumo: This paper proposes a new concept: the usage of Multivariate Markov Chains (MMC) as covariates. Our approach is based on the observation that we can treat possible categorical (or discrete) regressors, whose values are unknown in the forecast period, as an MMC in order to improve the forecast error of a certain dependent variable. Hence, we take advantage of the information about the past state interactions between the MMC categories to forecast the categorical (or discrete) regressors and improve the forecast of the actual dependent variable.
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spelling Combining a regression model with a multivariate Markov chain in a forecasting problemMultivariate Markov ChainHigher-order Markov ChainMarkov Chain ForecastingThis paper proposes a new concept: the usage of Multivariate Markov Chains (MMC) as covariates. Our approach is based on the observation that we can treat possible categorical (or discrete) regressors, whose values are unknown in the forecast period, as an MMC in order to improve the forecast error of a certain dependent variable. Hence, we take advantage of the information about the past state interactions between the MMC categories to forecast the categorical (or discrete) regressors and improve the forecast of the actual dependent variable.Elsevier B.V.Repositório da Universidade de LisboaDamásio, BrunoNicolau, João2023-04-03T08:34:02Z20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27570engDamásio, Bruno and João Nicolau .(2014). “Combining a regression model with a multivariate Markov chain in a forecasting problem”. Statistics & Probability Letters, Volume 90: pp. 108-1130167-715210.1016/j.spl.2014.03.026info: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:RCAAP2023-04-09T01:32:09Zoai:www.repository.utl.pt:10400.5/27570Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:49:02.283847Repositório 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 Combining a regression model with a multivariate Markov chain in a forecasting problem
title Combining a regression model with a multivariate Markov chain in a forecasting problem
spellingShingle Combining a regression model with a multivariate Markov chain in a forecasting problem
Damásio, Bruno
Multivariate Markov Chain
Higher-order Markov Chain
Markov Chain Forecasting
title_short Combining a regression model with a multivariate Markov chain in a forecasting problem
title_full Combining a regression model with a multivariate Markov chain in a forecasting problem
title_fullStr Combining a regression model with a multivariate Markov chain in a forecasting problem
title_full_unstemmed Combining a regression model with a multivariate Markov chain in a forecasting problem
title_sort Combining a regression model with a multivariate Markov chain in a forecasting problem
author Damásio, Bruno
author_facet Damásio, Bruno
Nicolau, João
author_role author
author2 Nicolau, João
author2_role author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Damásio, Bruno
Nicolau, João
dc.subject.por.fl_str_mv Multivariate Markov Chain
Higher-order Markov Chain
Markov Chain Forecasting
topic Multivariate Markov Chain
Higher-order Markov Chain
Markov Chain Forecasting
description This paper proposes a new concept: the usage of Multivariate Markov Chains (MMC) as covariates. Our approach is based on the observation that we can treat possible categorical (or discrete) regressors, whose values are unknown in the forecast period, as an MMC in order to improve the forecast error of a certain dependent variable. Hence, we take advantage of the information about the past state interactions between the MMC categories to forecast the categorical (or discrete) regressors and improve the forecast of the actual dependent variable.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01T00:00:00Z
2023-04-03T08:34:02Z
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/10400.5/27570
url http://hdl.handle.net/10400.5/27570
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Damásio, Bruno and João Nicolau .(2014). “Combining a regression model with a multivariate Markov chain in a forecasting problem”. Statistics & Probability Letters, Volume 90: pp. 108-113
0167-7152
10.1016/j.spl.2014.03.026
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
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
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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