arXiv subjects

Karl L. Hallgren

Publications and source records attributed to Karl L. Hallgren.

2 recordsLinked to original sources

arXiv2023

Changepoint detection on a graph of time series

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 changepoints. This article proposes an informative prior for changepoints which encodes the information contained in the graph, inducing a changepoint model for multiple time series that borrows strength across clusters of connected time series to detect weak signals for synchronous changepoints. The graphical model for changepoints is further extended to allow dependence between nearby but not necessarily synchronous changepoints across neighbouring time series in the graph. A novel reversible jump Markov chain Monte Carlo (MCMC) algorithm making use of auxiliary variables is proposed to sample from the graphical changepoint model. 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.

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arXiv2021

Changepoint detection in non-exchangeable data

Changepoint models typically assume the data within each segment are independent and identically distributed conditional on some parameters which change across segments. This construction may be inadequate when data are subject to local correlation patterns, often resulting in many more changepoints fitted than preferable. This article proposes a Bayesian changepoint model which relaxes the assumption of exchangeability within segments. The proposed model supposes data within a segment are $m$-dependent for some unkown $m \geqslant0$ which may vary between segments, resulting in a model suitable for detecting clear discontinuities in data which are subject to different local temporal correlations. The approach is suited to both continuous and discrete data. A novel reversible jump MCMC algorithm is proposed to sample from the model; in particular, a detailed analysis of the parameter space is exploited to build proposals for the orders of dependence. Two applications demonstrate the benefits of the proposed model: computer network monitoring via change detection in count data, and segmentation of financial time series.

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