Explainable multi-class anomaly detection on functional data

@article{Cura2022ExplainableMA,
  title={Explainable multi-class anomaly detection on functional data},
  author={Mathieu Cura and Katarina Firdova and C{\'e}line Labart and Arthur Martel},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.02935}
}
. 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. 
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