When analysing multiple time series that may be subject to changepoints, it is sometimes possible to specify a priori, by means of a graph G, which pairs of time series are likely to be impacted by simultaneous changepoints. This article proposes a novel Bayesian changepoint model for multiple time series that borrows strength across clusters of connected time series in G to detect weak signals for synchronous changepoints. The graphical changepoint model is further extended to allow dependence between nearby but not necessarily synchronous changepoints across neighbour time series in G. A novel reversible jump MCMC algorithm making use of auxiliary variables is proposed to sample from the graphical changepoint model. The merit of the proposed model is demonstrated via a changepoint analysis of real network authentication data from Los Alamos National Laboratory (LANL), with some success at detecting weak signals for network intrusions across users that are linked by network connectivity, whilst limiting the number of false alerts.

翻译：在分析可能受到变化点影响的多个时间序列时,有时可以通过图表G指定一个先验性的时间序列,其中对应的时间序列可能会受到同时变化点的影响。本篇文章提议了一个新的贝叶西亚变化点模式,用于多个时间序列,在G组连接的时间序列中借出强度,以探测同步变化点的弱信号。图形变化点模式进一步扩展,允许附近之间依赖,但不一定是相邻时间序列之间的同步变化点。向图形变化点模型样本建议了利用辅助变量的新颖的可逆跳动MCMC算法。拟议模型的优点是通过对洛斯阿拉莫斯国家实验室(Los Alamos National Laurational Laural(L))真实网络认证数据的变化点分析来证明的,在发现网络连接连接的用户网络入侵的弱信号方面有些成功,同时限制虚假警报的数量。