Wavelet-Based Detection of Outliers in Poisson INAR(1) Time Series
| Main Author: | |
|---|---|
| Publication Date: | 2018 |
| Other Authors: | |
| Format: | Book |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | https://hdl.handle.net/10216/111744 |
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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Wavelet-Based Detection of Outliers in Poisson INAR(1) Time SeriesThe 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.20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/111744eng10.1007/978-3-319-76605-8_13Isabel SilvaMaria Eduarda Silvainfo: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:RCAAP2025-02-27T18:10:45Zoai:repositorio-aberto.up.pt:10216/111744Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T22:40:12.111653Repositórios 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 Isabel Silva |
| 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 |
Isabel Silva |
| author_facet |
Isabel Silva Maria Eduarda Silva |
| author_role |
author |
| author2 |
Maria Eduarda Silva |
| author2_role |
author |
| dc.contributor.author.fl_str_mv |
Isabel Silva Maria Eduarda Silva |
| 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 2018-01-01T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/book |
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book |
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publishedVersion |
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https://hdl.handle.net/10216/111744 |
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https://hdl.handle.net/10216/111744 |
| dc.language.iso.fl_str_mv |
eng |
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eng |
| dc.relation.none.fl_str_mv |
10.1007/978-3-319-76605-8_13 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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info@rcaap.pt |
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1833599800740675584 |