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A0345
Title: Changepoint inference with a graphical dependence structure Authors:  Nick Heard - Imperial College London (United Kingdom) [presenting]
Abstract: When analysing multiple time series that may be subject to changepoints, it is sometimes possible to specify a priori, by means of a graph, which pairs of time series are likely to be impacted by simultaneous changes. An informative prior distribution for changepoints is introduced which encodes the information from the graph, providing a changepoint model for multiple time series that borrows strength across clusters of connected time series to detect weak signals for synchronous changepoints. The approach is extended to allow dependence between nearby but not necessarily synchronous changepoints across neighbouring time series in the graph. For inference, we use an adaptation of the partial decoupling method for auxiliary variable reversible jump MCMC. The merit of the proposed approach is demonstrated through a changepoint analysis of computer network authentication logs from Los Alamos National Laboratory (LANL), demonstrating an improvement at detecting weak signals for network intrusions across users linked by network connectivity, whilst limiting the number of false alerts.