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Change detection involves segmenting sequential data such that observationsin the same segment share some desired properties. Multivariate changedetection continues to be a challenging problem due to the variety of wayschange points can be correlated across channels and the potentially poorsignal-to-noise ratio on individual channels. In this paper, we are interestedin locating additive outliers (AO) and level shifts (LS) in the unsupervisedsetting. We propose ABACUS, Automatic BAyesian Changepoints Under Sparsity, aBayesian source separation technique to recover latent signals while alsodetecting changes in model parameters. Multi-level sparsity achieves bothdimension reduction and modeling of signal changes. We show ABACUS hascompetitive or superior performance in simulation studies againststate-of-the-art change detection methods and established latent variablemodels. We also illustrate ABACUS on two real application, modeling genomicprofiles and analyzing household electricity consumption.

ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation