A note on moving average forecasts of long memory processes with an application to quality control

Detalhes bibliográficos
Autor(a) principal: Ramjee, Radhika
Data de Publicação: 2002
Outros Autores: Crato, Nuno, Ray, Bonnie K.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/27678
Resumo: Standard quality control chart interpretation assumes that the observed data are uncorrelated. The presence of autocorrelation in process data has adverse effects on the performance of control charts. The objective of this paper is to assess the behavior of moving average forecast-based control charts on data having correlation that is persistent over very long time horizons, i.e., long-range dependent. We show that charts based on exponentially weighted moving average (EWMA) prediction do not perform well at detecting process shifts in long-range dependent data. We then introduce a new type of control chart, the hyperbolically weighted moving average (HWMA) chart, designed specifically for long-range dependent data. The HWMA charts perform better than the EWMA charts at detecting changes in the level of a long-memory process and also provide competitive performance for process data having only short-range dependence.
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spelling A note on moving average forecasts of long memory processes with an application to quality controlAutocorrelationControl ChartsLong-range DependenceTime SeriesStandard quality control chart interpretation assumes that the observed data are uncorrelated. The presence of autocorrelation in process data has adverse effects on the performance of control charts. The objective of this paper is to assess the behavior of moving average forecast-based control charts on data having correlation that is persistent over very long time horizons, i.e., long-range dependent. We show that charts based on exponentially weighted moving average (EWMA) prediction do not perform well at detecting process shifts in long-range dependent data. We then introduce a new type of control chart, the hyperbolically weighted moving average (HWMA) chart, designed specifically for long-range dependent data. The HWMA charts perform better than the EWMA charts at detecting changes in the level of a long-memory process and also provide competitive performance for process data having only short-range dependence.ElsevierRepositório da Universidade de LisboaRamjee, RadhikaCrato, NunoRay, Bonnie K.2023-04-28T18:04:28Z20022002-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/27678engRamjee, Radhika, Nuno Crato and Bonnie K. Ray. "A note on moving average forecasts of long memory processes with an application to quality control". International Journal of Forecasting, Vol. 18, No. 2: pp. 291-297. (Search PDF in 2023).0169-2070info: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:RCAAP2023-04-30T01:30:59Zoai:www.repository.utl.pt:10400.5/27678Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:50:29.302762Repositório 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 A note on moving average forecasts of long memory processes with an application to quality control
title A note on moving average forecasts of long memory processes with an application to quality control
spellingShingle A note on moving average forecasts of long memory processes with an application to quality control
Ramjee, Radhika
Autocorrelation
Control Charts
Long-range Dependence
Time Series
title_short A note on moving average forecasts of long memory processes with an application to quality control
title_full A note on moving average forecasts of long memory processes with an application to quality control
title_fullStr A note on moving average forecasts of long memory processes with an application to quality control
title_full_unstemmed A note on moving average forecasts of long memory processes with an application to quality control
title_sort A note on moving average forecasts of long memory processes with an application to quality control
author Ramjee, Radhika
author_facet Ramjee, Radhika
Crato, Nuno
Ray, Bonnie K.
author_role author
author2 Crato, Nuno
Ray, Bonnie K.
author2_role author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Ramjee, Radhika
Crato, Nuno
Ray, Bonnie K.
dc.subject.por.fl_str_mv Autocorrelation
Control Charts
Long-range Dependence
Time Series
topic Autocorrelation
Control Charts
Long-range Dependence
Time Series
description Standard quality control chart interpretation assumes that the observed data are uncorrelated. The presence of autocorrelation in process data has adverse effects on the performance of control charts. The objective of this paper is to assess the behavior of moving average forecast-based control charts on data having correlation that is persistent over very long time horizons, i.e., long-range dependent. We show that charts based on exponentially weighted moving average (EWMA) prediction do not perform well at detecting process shifts in long-range dependent data. We then introduce a new type of control chart, the hyperbolically weighted moving average (HWMA) chart, designed specifically for long-range dependent data. The HWMA charts perform better than the EWMA charts at detecting changes in the level of a long-memory process and also provide competitive performance for process data having only short-range dependence.
publishDate 2002
dc.date.none.fl_str_mv 2002
2002-01-01T00:00:00Z
2023-04-28T18:04:28Z
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/10400.5/27678
url http://hdl.handle.net/10400.5/27678
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ramjee, Radhika, Nuno Crato and Bonnie K. Ray. "A note on moving average forecasts of long memory processes with an application to quality control". International Journal of Forecasting, Vol. 18, No. 2: pp. 291-297. (Search PDF in 2023).
0169-2070
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
publisher.none.fl_str_mv Elsevier
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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