Web: http://arxiv.org/abs/2205.02935

May 9, 2022, 1:11 a.m. | Mathieu Cura, Katarina Firdova, Céline Labart, Arthur Martel

cs.LG updates on arXiv.org arxiv.org

In this paper we describe an approach for anomaly detection and its
explainability in multivariate functional data. The anomaly detection procedure
consists of transforming the series into a vector of features and using an
Isolation forest algorithm. The explainable procedure is based on the
computation of the SHAP coefficients and on the use of a supervised decision
tree. We apply it on simulated data to measure the performance of our method
and on real data coming from industry.

anomaly detection arxiv data detection ml on

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