  Change-point detection (CPD) aims to detect abrupt changes over time series
data. Intuitively, effective CPD over multivariate time series should require
explicit modeling of the dependencies across input variables. However, existing
CPD methods either ignore the dependency structures entirely or rely on the
(unrealistic) assumption that the correlation structures are static over time.
In this paper, we propose a Correlation-aware Dynamics Model for CPD, which
explicitly models the correlation structure and dynamics of variables by
incorporating graph neural networks into an encoder-decoder framework.
Extensive experiments on synthetic and real-world datasets demonstrate the
advantageous performance of the proposed model on CPD tasks over strong
baselines, as well as its ability to classify the change-points as correlation
changes or independent changes. Keywords: Multivariate Time Series,
Change-point Detection, Graph Neural Networks
