Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models

Detalhes bibliográficos
Autor(a) principal: Souza, A. M.
Data de Publicação: 2012
Outros Autores: Souza, F. M., Menezes, R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://ciencia.iscte-iul.pt/id/ci-pub-38423
http://hdl.handle.net/10071/14276
Resumo: Technological development and production processes require statistical process control in the use of alternative techniques to evaluate a productive process. This paper proposes an alternative procedure for monitoring a multivariate productive process using residuals obtained from the principal component scores modeled by the general class of autoregressive integrated moving average (ARIMA) and the generalized autoregressive conditional heteroskedasticity (GARCH) processes. We seek to obtain and investigate non-correlated and independent residuals by means of X-bar and exponentially weighted moving average (EWMA) charts as a way to capture large and small variations in the productive process. The principal component analysis deals with the correlation among the variables and reduces the dimensions. The ARIMA-GARCH model estimates the mean and volatility of the principal components selected, providing independent residuals that are analyzed using control charts. Thus, a multivariate process can be assessed using univariate techniques, taking into account both the mean and the volatility behavior of the process. Therefore, we present an alternative procedure to evaluate a process with multivariate features to determine the level of volatility persistence in the productive process when an external action occurs.
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spelling Procedure to evaluate multivariate statistical process control using ARIMA-ARCH modelsARIMA modelsAutocorrelated processGARCH modelsMultivariate statistical process controlResidual control chartStatistical process controlVolatilityTechnological development and production processes require statistical process control in the use of alternative techniques to evaluate a productive process. This paper proposes an alternative procedure for monitoring a multivariate productive process using residuals obtained from the principal component scores modeled by the general class of autoregressive integrated moving average (ARIMA) and the generalized autoregressive conditional heteroskedasticity (GARCH) processes. We seek to obtain and investigate non-correlated and independent residuals by means of X-bar and exponentially weighted moving average (EWMA) charts as a way to capture large and small variations in the productive process. The principal component analysis deals with the correlation among the variables and reduces the dimensions. The ARIMA-GARCH model estimates the mean and volatility of the principal components selected, providing independent residuals that are analyzed using control charts. Thus, a multivariate process can be assessed using univariate techniques, taking into account both the mean and the volatility behavior of the process. Therefore, we present an alternative procedure to evaluate a process with multivariate features to determine the level of volatility persistence in the productive process when an external action occurs.Nihon Keikei Kogakkai2017-08-10T13:27:32Z2012-01-01T00:00:00Z20122017-08-10T11:58:15Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://ciencia.iscte-iul.pt/id/ci-pub-38423http://hdl.handle.net/10071/14276eng0386-4812Souza, A. M.Souza, F. M.Menezes, R.info: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:RCAAP2023-11-09T17:41:57Zoai:repositorio.iscte-iul.pt:10071/14276Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:19:33.656321Repositório 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 Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
title Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
spellingShingle Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
Souza, A. M.
ARIMA models
Autocorrelated process
GARCH models
Multivariate statistical process control
Residual control chart
Statistical process control
Volatility
title_short Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
title_full Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
title_fullStr Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
title_full_unstemmed Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
title_sort Procedure to evaluate multivariate statistical process control using ARIMA-ARCH models
author Souza, A. M.
author_facet Souza, A. M.
Souza, F. M.
Menezes, R.
author_role author
author2 Souza, F. M.
Menezes, R.
author2_role author
author
dc.contributor.author.fl_str_mv Souza, A. M.
Souza, F. M.
Menezes, R.
dc.subject.por.fl_str_mv ARIMA models
Autocorrelated process
GARCH models
Multivariate statistical process control
Residual control chart
Statistical process control
Volatility
topic ARIMA models
Autocorrelated process
GARCH models
Multivariate statistical process control
Residual control chart
Statistical process control
Volatility
description Technological development and production processes require statistical process control in the use of alternative techniques to evaluate a productive process. This paper proposes an alternative procedure for monitoring a multivariate productive process using residuals obtained from the principal component scores modeled by the general class of autoregressive integrated moving average (ARIMA) and the generalized autoregressive conditional heteroskedasticity (GARCH) processes. We seek to obtain and investigate non-correlated and independent residuals by means of X-bar and exponentially weighted moving average (EWMA) charts as a way to capture large and small variations in the productive process. The principal component analysis deals with the correlation among the variables and reduces the dimensions. The ARIMA-GARCH model estimates the mean and volatility of the principal components selected, providing independent residuals that are analyzed using control charts. Thus, a multivariate process can be assessed using univariate techniques, taking into account both the mean and the volatility behavior of the process. Therefore, we present an alternative procedure to evaluate a process with multivariate features to determine the level of volatility persistence in the productive process when an external action occurs.
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2012
2017-08-10T13:27:32Z
2017-08-10T11:58:15Z
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 https://ciencia.iscte-iul.pt/id/ci-pub-38423
http://hdl.handle.net/10071/14276
url https://ciencia.iscte-iul.pt/id/ci-pub-38423
http://hdl.handle.net/10071/14276
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0386-4812
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 Nihon Keikei Kogakkai
publisher.none.fl_str_mv Nihon Keikei Kogakkai
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
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