  Anomaly detection on multivariate time-series is of great importance in both
data mining research and industrial applications. Recent approaches have
achieved significant progress in this topic, but there is remaining
limitations. One major limitation is that they do not capture the relationships
between different time-series explicitly, resulting in inevitable false alarms.
In this paper, we propose a novel self-supervised framework for multivariate
time-series anomaly detection to address this issue. Our framework considers
each univariate time-series as an individual feature and includes two graph
attention layers in parallel to learn the complex dependencies of multivariate
time-series in both temporal and feature dimensions. In addition, our approach
jointly optimizes a forecasting-based model and are construction-based model,
obtaining better time-series representations through a combination of
single-timestamp prediction and reconstruction of the entire time-series. We
demonstrate the efficacy of our model through extensive experiments. The
proposed method outperforms other state-of-the-art models on three real-world
datasets. Further analysis shows that our method has good interpretability and
is useful for anomaly diagnosis.
