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Outlier Detection for Multivariate Time Series: A Functional Data Approach ®
dc.contributor.author | López-Oriona, Ángel | |
dc.contributor.author | Vilar, José | |
dc.date.accessioned | 2022-01-13T20:01:06Z | |
dc.date.available | 2022-01-13T20:01:06Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Ángel López-Oriona, José A. Vilar, Outlier detection for multivariate time series: A functional data approach, Knowledge-Based Systems, Volume 233, 2021, 107527, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2021.107527. (https://www.sciencedirect.com/science/article/pii/S0950705121007899) | |
dc.identifier.uri | http://hdl.handle.net/2183/29382 | |
dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | |
dc.description.abstract | [Abstract] A method for detecting outlier samples in a multivariate time series dataset is proposed. It is assumed that an outlying series is characterized by having been generated from a different process than those associated with the rest of the series. Each multivariate time series is described by means of an estimator of its quantile cross-spectral density, which is treated as a multivariate functional datum. Then an outlier score is assigned to each series by using functional depths. A broad simulation study shows that the proposed approach is superior to the alternatives suggested in the literature and demonstrates that the consideration of functional data constitutes a critical step. The procedure runs in linear time with respect to both the series length and the number of series, and in quadratic time with respect to the number of dimensions. Two applications concerning financial series and ECG signals highlight the usefulness of the technique. | es_ES |
dc.description.sponsorship | This research has been supported by the Ministerio de Economía y Competitividad (MINECO) grants MTM2017-82724-R and PID2020-113578RB-100, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14), and the Centro de Investigación del Sistema Universitario de Galicia “CITIC” grant ED431G 2019/01; all of them through the European Regional Development Fund (ERDF). This work has received funding for open access charge by Universidade da Coruña/CISUG . | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION/ | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | |
dc.relation.uri | https://doi.org/10.1016/j.knosys.2021.107527 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Multivariate time series | es_ES |
dc.subject | Quantile cross-spectral density | es_ES |
dc.subject | Outliers | es_ES |
dc.subject | Functional data | es_ES |
dc.subject | Functional depth | es_ES |
dc.subject | Financial series | es_ES |
dc.subject | ECG signals | es_ES |
dc.title | Outlier Detection for Multivariate Time Series: A Functional Data Approach ® | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Knowledge-Based Systems | es_ES |
UDC.volume | 233 | es_ES |
UDC.startPage | 107527 | es_ES |
dc.identifier.doi | 10.1016/j.knosys.2021.107527 |
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