Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/66160
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dc.creatorJoaquim F. Pinto da Costa
dc.creatorIsabel Silva
dc.creatorM. Eduarda Silva
dc.date.accessioned2022-09-15T04:03:47Z-
dc.date.available2022-09-15T04:03:47Z-
dc.date.issued2007
dc.identifier.othersigarra:69456
dc.identifier.urihttps://hdl.handle.net/10216/66160-
dc.description.abstractIn this work we consider the problem of clustering time series. Contrary to other works on this topic, our main concern is to let the most important observations, for instance the most recent, have a larger weight on the analysis. This is done by defining similarities measures between two time series, based on Pearson's correlation coefficient, which uses the notion of weighted mean and weighted covariance, where the weights increase monotonically with the time. We use these measures, which are metrics between time series, as a similarity or dissimilarity index between the $n$ time series to be clustered. We apply a very well known partitional method, the K-means, with some adaptations to make it able to choose the number of clusters.
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Session of the ISI
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectEstatística, Matemática
dc.subjectStatistics, Mathematics
dc.titleTime dependent clustering of time series
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.contributor.uportoFaculdade de Economia
dc.contributor.uportoFaculdade de Ciências
dc.subject.fosCiências exactas e naturais::Matemática
dc.subject.fosNatural sciences::Mathematics
Appears in Collections:FCUP - Artigo em Livro de Atas de Conferência Internacional
FEP - Artigo em Livro de Atas de Conferência Internacional
FEUP - Artigo em Livro de Atas de Conferência Internacional

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