Abstract: Abstract: In many complex systems， devices are typically monitored by network sensors and actuators， generating a large amount of multivariate time series data. Accurately capturing the intricate relationships among sensors and detecting and elucidating anomalies that deviate from these interconnections has become a critical challenge that the current technological field must address. To fully utilize the spatial-temporal dependency relationships and enhance the interpretability of anomalies， this paper proposes a method of Multivariate Time Series Anomaly Detection Based on Hybrid Graph Attention Network （HGAT）. Firstly， HGAT constructs a Feature Graph Attention Network （F-GAT） and a Temporal Graph Attention Network （T-GAT） using embedding vector similarity. Subsequently， the non-linear dependency relationships of variable dimensions and temporal dimensions are learned by parallelly applying the two graph attention layers， F-GAT and T-GAT. Finally， HGAT facilitates the co-optimization of the prediction-based and reconstruction-based models. It employs the anomaly scores derived from this collaborative optimization to delineate aberrant instances， thereby enhancing the explicability of the anomaly detection mechanism. Empirical evaluations conducted on the SWaT， WADI， and SMD datasets demonstrate that the proposed HGAT algorithm exhibits superior performance compared to the state-of-the-art baseline， the GDN method， yielding enhancements in the F1 metrics by 2.73 percentage points， 3.39 percentage points， and 0.9 percentage points for each dataset， respectively.

Key words: Key words: multivariate time series, anomaly detection, graph neural networks, attention mechanism