This work develops techniques for the sequential detection and location
estimation of transient changes in the volatility (standard deviation) of time
series data. In particular, we introduce a class of change detection algorithms
based on the windowed volatility filter. The first method detects changes by
employing a convex combination of two such filters with differing window sizes,
such that the adaptively updated convex weight parameter is then used as an
indicator for the detection of instantaneous power changes. Moreover, the
proposed adaptive filtering based method is readily extended to the
multivariate case by using recent advances in distributed adaptive filters,
thereby using cooperation between the data channels for more effective
detection of change points. Furthermore, this work also develops a novel change
point location estimator based on the differenced output of the volatility
filter. Finally, the performance of the proposed methods were evaluated on both
synthetic and real world data.