  In this paper easily applicable techniques are devised for detecting
changepoints in autocorrelated Gaussian sequences. Our method proceeds by
sequential evaluation of a CUSUM-type test statistic, which is compared to a
predefined threshold. We assume that data is tested in sliding windows of fixed
size. The distinguishing feature of this work is that, based on large
deviations theory, we derive rather explicit equations that determine the
threshold in such a way that the false alarm probability per window is
approximately kept at the desired level. This criterion -- as opposed to the
usual average run length -- allows to restrict not only the average number of
false alarms but also their variability. Illustrative examples are provided,
including the detection of a shift in mean in ARMA processes. The procedures
are validated by means of a broad set of simulation experiments, and overall
perform well.
