Clive Cheong-Took

Alireza Ahrabian, Nazli Farajidavar, Clive Cheong-Took et al.

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.

Alireza Ahrabian, Shirin Enshaeifar, Clive Cheong-Took et al.

This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, which can improve performance. This work proposes a Bayesian mean-shift change point detection algorithm that makes use of repetition in segment parameters, by introducing segment class labels that utilise a Dirichlet process prior. The performance of the proposed approach was assessed on both synthetic and real world data, highlighting the enhanced performance when using parameter labelling.