  Monitoring complex systems results in massive multivariate time series data,
and anomaly detection of these data is very important to maintain the normal
operation of the systems. Despite the recent emergence of a large number of
anomaly detection algorithms for multivariate time series, most of them ignore
the correlation modeling among multivariate, which can often lead to poor
anomaly detection results. In this work, we propose a novel anomaly detection
model for multivariate time series with \underline{HI}gh-order
\underline{F}eature \underline{I}nteractions (HIFI). More specifically, HIFI
builds multivariate feature interaction graph automatically and uses the graph
convolutional neural network to achieve high-order feature interactions, in
which the long-term temporal dependencies are modeled by attention mechanisms
and a variational encoding technique is utilized to improve the model
performance and robustness. Extensive experiments on three publicly available
datasets demonstrate the superiority of our framework compared with
state-of-the-art approaches.
