Publicação
Measuring extremal clustering in time series
| 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. |
|---|---|
| Autores principais: | Ferreira, Marta Susana |
| Assunto: | Extremal index Extreme values theory Stationary sequences |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1867438860856721408 |
|---|---|
| author | Ferreira, Marta Susana |
| author_facet | Ferreira, Marta Susana |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Ferreira, Marta Susana\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Ferreira, Marta Susana |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-25T16:23:30Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-25T16:23:30Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Extremal index Extreme values theory Stationary sequences |
| datacite.titles.title.fl_str_mv | Measuring extremal clustering in time series |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Ferreira, Marta Susana |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-25T16:23:30Z |
| dc.date.embargoed.fl_str_mv | 2024-01-25T16:23:30Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/88296 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Multidisciplinary Digital Publishing Institute (MDPI) |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.subject.none.fl_str_mv | Extremal index Extreme values theory Stationary sequences |
| dc.title.fl_str_mv | Measuring extremal clustering in time series |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorium.uminho.pt/bitstreams/f8f7d7cf-a01c-42f4-8820-c30d4cb1053c/download |
| id | rum_7990b11b8bf95007a8ef4bd89efd2eea |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/88296 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/88296 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Ferreira, Marta Susana |
| publishDate | 2023 |
| publisher.none.fl_str_mv | Multidisciplinary Digital Publishing Institute (MDPI) |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engMultidisciplinary Digital Publishing Institute (MDPI)porThe 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.application/pdfporMeasuring extremal clustering in time seriesFerreira, Marta SusanaHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptARTICLENUMBER64ISSNIsPartOf2673-4591DOIIsPartOf10.3390/engproc20230390642024-01-25T16:23:30Z20232024-01-22T09:50:32Z2023-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/88296http://purl.org/coar/access_right/c_abf2open accessExtremal indexExtreme values theoryStationary sequences703808 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal article2023http://creativecommons.org/licenses/by/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorium.uminho.pt/bitstreams/f8f7d7cf-a01c-42f4-8820-c30d4cb1053c/download |
| spellingShingle | Measuring extremal clustering in time series Ferreira, Marta Susana Extremal index Extreme values theory Stationary sequences |
| status | SINGLETON |
| subject.fl_str_mv | Extremal index Extreme values theory Stationary sequences |
| title | 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_short | Measuring extremal clustering in time series |
| title_sort | Measuring extremal clustering in time series |
| topic | Extremal index Extreme values theory Stationary sequences |
| topic_facet | Extremal index Extreme values theory Stationary sequences |
| url | https://hdl.handle.net/1822/88296 |
| visible | 1 |