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Affinity coefficient for clustering autoregressive moving average models

2024-05-24Journal article Open access
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dc.contributor.authorNascimento, Ana Paula
dc.contributor.authorOliveira, Alexandra
dc.contributor.authorFaria, Brígida Mónica
dc.contributor.authorPimenta, Rui
dc.contributor.authorVieira, Mónica
dc.contributor.authorPrudêncio, Cristina
dc.contributor.authorNicolau, Helena Bacelar
dc.date.accessioned2024-10-09T10:04:45Z
dc.date.available2024-10-09T10:04:45Z
dc.date.issued2024-05-24
dc.description.abstractIn various fields, such as economics, finance, bioinformatics, geology, and medicine, namely, in the cases of electroencephalogram, electrocardiogram, and biotechnology, cluster analysis of time series is necessary. The first step in cluster applications is to establish a similarity/dissimilarity coefficient between time series. This article introduces an extension of the affinity coefficient for the autoregressive expansions of the invertible autoregressive moving average models to measure their similarity between them. An application of the affinity coefficient between time series was developed and implemented in R. Cluster analysis is performed with the corresponding distance for the estimated simulated autoregressive moving average of order one. The primary findings indicate that processes with similar forecast functions are grouped (in the same cluster) as expected concerning the affinity coefficient. It was also possible to conclude that this affinity coefficient is very sensitive to the behavior changes of the forecast functions: processes with small different forecast functions appear to be well separated in different clusters. Moreover, if the two processes have at least an infinite number of π- weights with a symmetric signal, the affinity value is also symmetric.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNascimento, A. P., Oliveira, A., Faria, B. M., Pimenta, R., Vieira, M., Prudêncio, C., & Bacelar-Nicolau, H. (2024). Affinity coefficient for clustering autoregressive moving average models. Computational and Mathematical Methods, 2024(1), 5540143. https://doi.org/10.1155/2024/5540143pt_PT
dc.identifier.doi10.1155/2024/5540143pt_PT
dc.identifier.eissn2577-7408
dc.identifier.urihttp://hdl.handle.net/10400.22/26223
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWileypt_PT
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/full/10.1155/2024/5540143pt_PT
dc.subjectAffinity coefficientpt_PT
dc.subjectClusteringpt_PT
dc.subjectDistancept_PT
dc.subjectSimilarity coefficientpt_PT
dc.subjectTime seriespt_PT
dc.titleAffinity coefficient for clustering autoregressive moving average modelspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleComputational and Mathematical Methodspt_PT
oaire.citation.volume2024 (1)pt_PT
person.familyNameNascimento
person.familyNameOliveira
person.familyNameFaria
person.familyNamePimenta
person.familyNameAlmeida Vieira
person.familyNamePrudêncio
person.givenNameAna Paula
person.givenNameAlexandra
person.givenNameBrigida Monica
person.givenNameRui
person.givenNameMónica Andreia
person.givenNameCristina
person.identifierR-000-T1F
person.identifier846820
person.identifier1200571
person.identifier.ciencia-id3A15-0245-285D
person.identifier.ciencia-id161A-55D9-C256
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person.identifier.ciencia-idD914-641B-E379
person.identifier.ciencia-idA01E-9178-9B48
person.identifier.ciencia-idC81E-F4EE-FADE
person.identifier.orcid0000-0002-4423-2706
person.identifier.orcid0000-0001-5872-5504
person.identifier.orcid0000-0003-2102-3407
person.identifier.orcid0000-0002-1985-8395
person.identifier.orcid0000-0002-8687-4811
person.identifier.orcid0000-0002-9920-936X
person.identifier.ridC-6649-2012
person.identifier.ridJUU-6485-2023
person.identifier.scopus-author-id56340903500
person.identifier.scopus-author-id6506476517
person.identifier.scopus-author-id56340941800
person.identifier.scopus-author-id6508057930
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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