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Multivariate Functional Outlier Detection using the FastMUOD Indices

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorOjo, Oluwasegun Taiwo
dc.contributor.authorFernández Anta, Antonio
dc.contributor.authorGenton, Marc G.
dc.contributor.authorLillo Rodríguez, Rosa Elvira
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2022-09-09T16:04:07Z
dc.date.available2022-09-09T16:04:07Z
dc.date.issued2022-09-09
dc.description.abstractWe present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detection in multivariate functional data are then proposed. These include applying FastMUOD on the components of the multivariate data and using random projections. Moreover, these techniques are tested on various simulated and real multivariate functional datasets. Compared with the state of the art in multivariate functional OD, the use of random projections showed the most effective results with similar, and in some cases improved, OD performance.en
dc.identifier.issn2387-0303es
dc.identifier.urihttps://hdl.handle.net/10016/35665
dc.identifier.uxxiDT/0000002023es
dc.language.isoengen
dc.relation.ispartofseriesWorking paper Statistics and Econometricses
dc.relation.ispartofseries22-09
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherFastmuoden
dc.subject.otherFunctional Dataen
dc.subject.otherFunctional Outlier Detectionen
dc.subject.otherMultivariate Functional Dataen
dc.subject.otherOutlier Classificationen
dc.subject.otherVideo Dataen
dc.titleMultivariate Functional Outlier Detection using the FastMUOD Indicesen
dc.typeworking paper*
dspace.entity.typePublication
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