Communication Dans Un Congrès Année : 2022

Outliers Detection in Functional Data

Irène Gannaz
Julien Jacques

Résumé

The modern technologies ease the collection of massive data at high frequency. From a statistical point of view, these data can be considered as functional data: discrete observations of random functions. One of the key problems in functional data analysis, is the detection of outliers. For this purpose, we propose a robust method based on contaminated Gaussian mixture models [1]. This model allows both to group and to detect outliers in multivariate functional data. A mixture of multivariate contaminated Gaussian distributions [2] is a Gaussian mixture where each cluster has two components: one, with a large prior probability, represents normal observations, and the other, with a small prior probability, represents outliers. Dimension reduction methods based on [3], are used to introduce parsimony into the model. An ECM (Expectation-Conditional Maximization) algorithm is proposed for model inference and the choice of hyper-parameters is addressed through model selection. The model performs efficiently on simulated data. It also helps to correctly detect outliers in the industrial data sets which motivated this work.

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Dates et versions

hal-04141050 , version 1 (03-09-2024)

Identifiants

  • HAL Id : hal-04141050 , version 1

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Martial Amovin-Assagba, Irène Gannaz, Julien Jacques. Outliers Detection in Functional Data. Classification and Data Science in the Digital Age - Book of Abstracts IFCS 2022, International Federation of Classification Societies, Jul 2022, Porto (Portugal), Portugal. pp.77. ⟨hal-04141050⟩
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