  Multimodal time series (MTS) anomaly detection is crucial for maintaining the
safety and stability of working devices (e.g., water treatment system and
spacecraft), whose data are characterized by multivariate time series with
diverse modalities. Although recent deep learning methods show great potential
in anomaly detection, they do not explicitly capture spatial-temporal
relationships between univariate time series of different modalities, resulting
in more false negatives and false positives. In this paper, we propose a
multimodal spatial-temporal graph attention network (MST-GAT) to tackle this
problem. MST-GAT first employs a multimodal graph attention network (M-GAT) and
a temporal convolution network to capture the spatial-temporal correlation in
multimodal time series. Specifically, M-GAT uses a multi-head attention module
and two relational attention modules (i.e., intra- and inter-modal attention)
to model modal correlations explicitly. Furthermore, MST-GAT optimizes the
reconstruction and prediction modules simultaneously. Experimental results on
four multimodal benchmarks demonstrate that MST-GAT outperforms the
state-of-the-art baselines. Further analysis indicates that MST-GAT strengthens
the interpretability of detected anomalies by locating the most anomalous
univariate time series.
