Abstract: In the task of anomaly detection for multivariate time series, the correlations between variables are complex, and such correlation is difficult to be clearly learned by traditional anomaly detection methods. In addition, the correlation between variables is only considered by most models, while time dependencies are not sufficiently addressed. Therefore, a time series anomaly detection model based on High-order Feature Aggregation (HFA) was proposed. Initially, a variable relationship graph was constructed through graph structure learning. Subsequently, the traditional Graph ATtention network (GAT) framework was enhanced by incorporating higher-order neighbor node correlations, enabling more accurate modeling of complex inter-variable relationships. Finally, temporal dependencies within sequences were further captured through the integration of one dimensional convolutional operations with self-attention mechanisms. Experimental evaluations conducted on four public datasets demonstrate that compared with the suboptimal baseline model Anomaly Transformer, the precision of the HFA method is increased by 3.92% on average, and the F1 score is improved by 1.34% on average. Compared with the current mainstream baseline method TopoGDN, the precision of the HFA method is increased by 6.05% on average, and the F1 score is improved by 9.05% on average. The results of the ablation experiment further verify the effectiveness of each module in the model.

Key words: multivariate time series, unsupervised learning, graph attention network, self-attention, anomaly detection

摘要： 在多变量时间序列异常检测任务中，不同变量之间的相关关系复杂，传统异常检测方法难以明确学习这种相关关系，且多数模型仅考虑变量之间的相关性，对时间依赖性的学习存在不足。因此，提出一种基于高阶特征聚合的时间序列异常检测模型（HFA）。首先，通过图结构学习构造变量之间的关系图。其次，在传统图注意力网络(GAT)的基础上进行改进，充分考虑高阶邻居节点的相关性，更准确地建模变量之间复杂的相关关系。最后，通过融合一维卷积和自注意力机制，能够充分挖掘序列的时间依赖性。在4个公开数据集上的对比实验结果表明，与次优基线模型Anomaly Transformer相比，HFA方法的准确率指标平均提升3.92%，F1值指标平均提升1.34%；与当前主流基线方法TopoGDN(Topology Graph Deviation Network)相比，HFA方法的准确率指标平均提升6.05%，F1值指标平均提升9.05%；消融实验结果进一步验证了模型中各个模块的有效性。

关键词: 多变量时间序列, 无监督学习, 图注意力网络, 自注意力, 异常检测