Explainable Unsupervised Change-point Detection via Graph Neural Networks
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Change-point detection (CPD) aims at detecting the abrupt property changes lying behind time series data. The property changes in a multivariate time series often result from highly entangled reasons, ranging from independent changes of variables to correlation changes between variables. Learning to uncover the reasons behind the changes in an unsupervised setting is a new and challenging task. Previous CPD methods usually detect change-points by a divergence estimation of statistical features, without delving into the reasons behind the detected changes. In this paper, we propose a correlation-aware dynamics model which separately predicts the correlation change and independent change by incorporating graph neural networks into the encoder-decoder framework. Through experiments on synthetic and real-world datasets, we demonstrate the enhanced performance of our model on the CPD tasks as well as its ability to interpret the nature and degree of the predicted changes.
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