Shape-based Outlier Detection in Multivariate Functional Data - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Knowledge-Based Systems Année : 2020

Shape-based Outlier Detection in Multivariate Functional Data

Résumé

Multivariate functional data refer to a population of multivariate functions generated by a system involving dynamic parameters depending on continuous variables (e.g., multivariate time series). Outlier detection in such a context is a challenging problem because both the individual behavior of the parameters and the dynamic correlation between them are important. To address this problem, recent work has focused on multivariate functional depth to identify the outliers in a given dataset. However, most previous approaches fail when the outlyingness manifests itself in curve shape rather than curve magnitude. In this paper, we propose identifying outliers in multivariate functional data by a method whereby different outlying features are captured based on mapping functions from differential geometry. In this regard, we extract shape features reflecting the outlyingness of a curve with a high degree of interpretability. We conduct an experimental study on real and synthetic data sets and compare the proposed method with functional-depth-based methods. The results demonstrate that the proposed method, combined with state-of-the-art outlier detection algorithms, can outperform the functional-depth-based methods. Moreover, in contrast with the baseline methods, it is efficient regardless of the proportion of outliers.
Fichier principal
Vignette du fichier
S0950705120302835.pdf (1.1 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02960358 , version 1 (22-08-2022)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

Citer

Clément Lejeune, Josiane Mothe, Adil Soubki, Olivier Teste. Shape-based Outlier Detection in Multivariate Functional Data. Knowledge-Based Systems, 2020, 198, pp.1-18. ⟨10.1016/j.knosys.2020.105960⟩. ⟨hal-02960358⟩
173 Consultations
90 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More