Outlier detection in multivariate functional data through a contaminated mixture model

Martial Amovin-Assagba, Irène Gannaz and Julien Jacques

Computational Statistics & Data Analysis, 2022, vol. 174, issue C

Abstract: In an industrial context, the activity of sensors is recorded at a high frequency. A challenge is to automatically detect abnormal measurement behavior. Considering the sensor measures as functional data, the problem can be formulated as the detection of outliers in a multivariate functional data set. Due to the heterogeneity of this data set, the proposed contaminated mixture model both clusters the multivariate functional data into homogeneous groups and detects outliers. The main advantage of this procedure over its competitors is that it does not require to specify the proportion of outliers. Model inference is performed through an Expectation-Conditional Maximization algorithm, and the BIC is used to select the number of clusters. Numerical experiments on simulated data demonstrate the high performance achieved by the inference algorithm. In particular, the proposed model outperforms the competitors. Its application on the real data which motivated this study allows to correctly detect abnormal behaviors.

Keywords: Outlier detection; Contaminated Gaussian mixture; Multivariate functional data; Model-based clustering; EM algorithm (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:174:y:2022:i:c:s0167947322000767

DOI: 10.1016/j.csda.2022.107496

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