Robust archetypoids for anomaly detection in big functional data
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Robust archetypoids for anomaly detection in big functional dataData de publicació
2020-08-03Editor
SpringerCita bibliogràfica
Vinue, G., Epifanio, I. Robust archetypoids for anomaly detection in big functional data. Adv Data Anal Classif (2020). https://doi.org/10.1007/s11634-020-00412-9Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://link.springer.com/article/10.1007/s11634-020-00412-9Versió
info:eu-repo/semantics/acceptedVersionParaules clau / Matèries
Resum
Archetypoid analysis (ADA) has proven to be a successful unsupervised statistical technique to identify extreme observations in the periphery of the data cloud, both in classical multivariate data and functional data. ... [+]
Archetypoid analysis (ADA) has proven to be a successful unsupervised statistical technique to identify extreme observations in the periphery of the data cloud, both in classical multivariate data and functional data. However, two questions remain open in this field: the use of ADA for outlier detection and its scalability. We propose to use robust functional archetypoids and adjusted boxplot to pinpoint functional outliers. Furthermore, we present a new archetypoid algorithm for obtaining results from large data sets in reasonable time. Functional time series are occurring in many practical problems, so this paper focuses on functional data settings. The new algorithm for detecting functional anomalies, called CRO-FADALARA, can be used with both univariate and multivariate curves. Our proposal for outlier detection is compared with all the state-of-the-art methods in a controlled study, showing a good performance. Furthermore, CRO-FADALARA is applied to two large time series data sets, where outliers curves are discussed and the reduction in computational time is clearly stated. A third case study with a small ECG data set is discussed, given its importance in functional data scenarios. All data, R code and a new R package are freely available. [-]
Proyecto de investigación
Research Foundation-Flanders (FWO-Vlaanderen), SBO grant HYMOP (150033) ; Spanish Ministry of Science, Innovation and Universities (AEI/FEDER, EU) (DPI2017-87333-R) ; Universitat Jaume I (UJI-B2017-13)Drets d'accés
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
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info:eu-repo/semantics/openAccess
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