A note on moving average forecasts of long memory processes with an application to quality control
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , |
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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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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1799131584686718976 |