Measuring extremal clustering in time series

Detalhes bibliográficos
Autor(a) principal: Ferreira, Marta Susana
Data de Publicação: 2023
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/88296
Resumo: The propensity of data to cluster at extreme values is important for risk assessment. For example, heavy rain over time leads to catastrophic floods. The extremal index is a measure of Extreme Values Theory that allows measurement of the degree of high-value clustering in a time series. Inference about the extremal index requires a prior choice of values for tuning parameters, which impacts the efficiency of existing estimators. In this work, we propose an algorithm that avoids these constraints. Performance is evaluated based on simulations. We also illustrate with real data.
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spelling Measuring extremal clustering in time seriesExtremal indexExtreme values theoryStationary sequencesThe propensity of data to cluster at extreme values is important for risk assessment. For example, heavy rain over time leads to catastrophic floods. The extremal index is a measure of Extreme Values Theory that allows measurement of the degree of high-value clustering in a time series. Inference about the extremal index requires a prior choice of values for tuning parameters, which impacts the efficiency of existing estimators. In this work, we propose an algorithm that avoids these constraints. Performance is evaluated based on simulations. We also illustrate with real data.The author was financed by Portuguese Funds through FCT - Fundação para a Ciência e a Tecnologia within the Projects UIDB/00013/2020 and UIDP/00013/2020 of Centre of Mathematics of the University of Minho.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoFerreira, Marta Susana20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/88296eng2673-459110.3390/engproc202303906464https://www.mdpi.com/2673-4591/39/1/64info: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:RCAAP2024-01-27T01:20:18Zoai:repositorium.sdum.uminho.pt:1822/88296Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:57:56.612870Repositório 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 Measuring extremal clustering in time series
title Measuring extremal clustering in time series
spellingShingle Measuring extremal clustering in time series
Ferreira, Marta Susana
Extremal index
Extreme values theory
Stationary sequences
title_short Measuring extremal clustering in time series
title_full Measuring extremal clustering in time series
title_fullStr Measuring extremal clustering in time series
title_full_unstemmed Measuring extremal clustering in time series
title_sort Measuring extremal clustering in time series
author Ferreira, Marta Susana
author_facet Ferreira, Marta Susana
author_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Ferreira, Marta Susana
dc.subject.por.fl_str_mv Extremal index
Extreme values theory
Stationary sequences
topic Extremal index
Extreme values theory
Stationary sequences
description The propensity of data to cluster at extreme values is important for risk assessment. For example, heavy rain over time leads to catastrophic floods. The extremal index is a measure of Extreme Values Theory that allows measurement of the degree of high-value clustering in a time series. Inference about the extremal index requires a prior choice of values for tuning parameters, which impacts the efficiency of existing estimators. In this work, we propose an algorithm that avoids these constraints. Performance is evaluated based on simulations. We also illustrate with real data.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
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://hdl.handle.net/1822/88296
url https://hdl.handle.net/1822/88296
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2673-4591
10.3390/engproc2023039064
64
https://www.mdpi.com/2673-4591/39/1/64
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 Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (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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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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