Wavelet-based detection of outliers in Poisson INAR(1) time series

Bibliographic Details
Main Author: Silva, Isabel
Publication Date: 2018
Other Authors: Silva, Maria Eduarda
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10773/35418
Summary: The presence of outliers or discrepant observations has a negative impact in time series modelling. This paper considers the problem of detecting outliers, additive or innovational, single, multiple or in patches, in count time series modelled by first-order Poisson integer-valued autoregressive, PoINAR(1), models. To address this problem, two wavelet-based approaches that allow the identification of the time points of outlier occurrence are proposed. The effectiveness of the proposed methods is illustrated with synthetic as well as with an observed dataset.
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spelling Wavelet-based detection of outliers in Poisson INAR(1) time seriesPoisson INAROutlier occurrenceCount time seriesAcceptance envelopeLevel detail coefficientsThe presence of outliers or discrepant observations has a negative impact in time series modelling. This paper considers the problem of detecting outliers, additive or innovational, single, multiple or in patches, in count time series modelled by first-order Poisson integer-valued autoregressive, PoINAR(1), models. To address this problem, two wavelet-based approaches that allow the identification of the time points of outlier occurrence are proposed. The effectiveness of the proposed methods is illustrated with synthetic as well as with an observed dataset.Springer2022-12-13T09:53:20Z2018-01-01T00:00:00Z2018book partinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10773/35418eng978-3-319-76604-110.1007/978-3-319-76605-8_13Silva, IsabelSilva, Maria Eduardainfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-06T04:40:32Zoai:ria.ua.pt:10773/35418Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:16:36.982010Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Wavelet-based detection of outliers in Poisson INAR(1) time series
title Wavelet-based detection of outliers in Poisson INAR(1) time series
spellingShingle Wavelet-based detection of outliers in Poisson INAR(1) time series
Silva, Isabel
Poisson INAR
Outlier occurrence
Count time series
Acceptance envelope
Level detail coefficients
title_short Wavelet-based detection of outliers in Poisson INAR(1) time series
title_full Wavelet-based detection of outliers in Poisson INAR(1) time series
title_fullStr Wavelet-based detection of outliers in Poisson INAR(1) time series
title_full_unstemmed Wavelet-based detection of outliers in Poisson INAR(1) time series
title_sort Wavelet-based detection of outliers in Poisson INAR(1) time series
author Silva, Isabel
author_facet Silva, Isabel
Silva, Maria Eduarda
author_role author
author2 Silva, Maria Eduarda
author2_role author
dc.contributor.author.fl_str_mv Silva, Isabel
Silva, Maria Eduarda
dc.subject.por.fl_str_mv Poisson INAR
Outlier occurrence
Count time series
Acceptance envelope
Level detail coefficients
topic Poisson INAR
Outlier occurrence
Count time series
Acceptance envelope
Level detail coefficients
description The presence of outliers or discrepant observations has a negative impact in time series modelling. This paper considers the problem of detecting outliers, additive or innovational, single, multiple or in patches, in count time series modelled by first-order Poisson integer-valued autoregressive, PoINAR(1), models. To address this problem, two wavelet-based approaches that allow the identification of the time points of outlier occurrence are proposed. The effectiveness of the proposed methods is illustrated with synthetic as well as with an observed dataset.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01T00:00:00Z
2018
2022-12-13T09:53:20Z
dc.type.driver.fl_str_mv book part
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/35418
url http://hdl.handle.net/10773/35418
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-3-319-76604-1
10.1007/978-3-319-76605-8_13
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 Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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