  We address the problem of online change detection in multivariate
datastreams, and we introduce QuantTree Exponentially Weighted Moving Average
(QT-EWMA), a nonparametric change-detection algorithm that can control the
expected time before a false alarm, yielding a desired Average Run Length
(ARL$_0$). Controlling false alarms is crucial in many applications and is
rarely guaranteed by online change-detection algorithms that can monitor
multivariate datastreams without knowing the data distribution. Like many
change-detection algorithms, QT-EWMA builds a model of the data distribution,
in our case a QuantTree histogram, from a stationary training set. To monitor
datastreams even when the training set is extremely small, we propose
QT-EWMA-update, which incrementally updates the QuantTree histogram during
monitoring, always keeping the ARL$_0$ under control. Our experiments,
performed on synthetic and real-world datastreams, demonstrate that QT-EWMA and
QT-EWMA-update control the ARL$_0$ and the false alarm rate better than
state-of-the-art methods operating in similar conditions, achieving lower or
comparable detection delays.
