关键词: 多变量时间序列, 异常检测, 深度学习, 时空信息, 图注意力网络

Abstract: Time series anomaly detection is a well-established research area within sequential tasks, achieving significant results in both academia and industry. Addressing the multi-dimensional deep features and complex inherent dependencies in multivariate time series data, a novel anomaly detection model integrating spatiotemporal features is proposed. The model employs a graph attention network structure composed of a Temporal Graph Attention Network (T-GAT) and a Spatial Graph Attention Network (F-GAT). T-GAT constructs a unidirectional weighted graph where edges represent temporal dependencies, simulating prior information about the temporal graph structure and integrating it into the network to capture time-related relationships. F-GAT converts time series into frequency domain sequences represented by amplitudes and establishes a global bi-directional weighted graph to simulate associations between multivariate elements, using regularization to maintain sparsity among neighboring nodes and ensure accurate capture of spatial relationships. The model incorporates a multi-dimensional attention mechanism to effectively mine and utilize deep features across different characteristics. A Gated Recurrent Unit further processes the spatiotemporal information, integrating it into comprehensive features, with anomalies identified by differences between predicted and observed values. Experimental results on four public datasets demonstrate that the model achieves advanced performance among twelve comparison models with superior F1 scores, and ablation studies confirm that the dual graph structure and attention mechanisms significantly enhance anomaly detection accuracy, effectively identifying anomalies in time series data.

Key words: Multivariate Time Series, Anomaly Detection, Deep Learning, Spatiotemporal Information, Graph Attention Networks