  The anomaly detection problem for univariate or multivariate time series is a
critical question in many practical applications as industrial processes
control, biological measures, engine monitoring, supervision of all kinds of
behavior. In this paper we propose a simple and empirical approach to detect
anomalies in the behavior of multivariate time series. The approach is based on
the empirical estimation of the conditional quantiles of the data, which
provides upper and lower bounds for the confidence tubes. The method is tested
on artificial data and its effectiveness has been proven in a real framework
such as that of the monitoring of aircraft engines.
