  Multivariate time-series anomaly detection is critically important in many
applications, including retail, transportation, power grid, and water treatment
plants. Existing approaches for this problem mostly employ either statistical
models which cannot capture the non-linear relations well or conventional deep
learning models (e.g., CNN and LSTM) that do not explicitly learn the pairwise
correlations among variables. To overcome these limitations, we propose a novel
method, correlation-aware spatial-temporal graph learning (termed CST-GL), for
time series anomaly detection. CST-GL explicitly captures the pairwise
correlations via a multivariate time series correlation learning module based
on which a spatial-temporal graph neural network (STGNN) can be developed.
Then, by employing a graph convolution network that exploits one- and multi-hop
neighbor information, our STGNN component can encode rich spatial information
from complex pairwise dependencies between variables. With a temporal module
that consists of dilated convolutional functions, the STGNN can further capture
long-range dependence over time. A novel anomaly scoring component is further
integrated into CST-GL to estimate the degree of an anomaly in a purely
unsupervised manner. Experimental results demonstrate that CST-GL can detect
anomalies effectively in general settings as well as enable early detection
across different time delays.
