  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.
