  The goal of anomaly detection is to identify observations that are generated
by a distribution that differs from the reference distribution that qualifies
normal behavior. When examining a time series, the reference distribution may
evolve over time. The anomaly detector must therefore be able to adapt to such
changes. In the online context, it is particularly difficult to adapt to abrupt
and unpredictable changes. Our solution to this problem is based on the
detection of breakpoints in order to adapt in real time to the new reference
behavior of the series and to increase the accuracy of the anomaly detection.
This solution also provides a control of the False Discovery Rate by extending
methods developed for stationary series.
