关键词: 异常检测, 多元时间序列, 注意力机制, 变分自编码器, 频域增强

Abstract: Time series anomaly detection is an important research topic in the field of time series analysis. Due to the complex spatiotemporal dependencies and randomness of multivariate time series in real industrial scenarios, many existing anomaly detection methods for single dependency modeling cannot effectively learn data features. In addition, ignoring frequency domain information will also lead to incomplete model feature representation. To address the above problems, a time series anomaly detection model FeGvL (Frequency-domain enhancement Graph-variational Learning) based on frequency domain enhanced graph variational learning networks was proposed. First, after the block operation, the dependency in the time dimension was modeled by self-attention; secondly, the graph relationship features after frequency domain enhancement were mapped to the latent space; Finally, the graph aggregation attention network was used to extract features between entities, and the temporal dependency was combined to achieve generalized variational reconstruction. The performance of the proposed model was verified on three public datasets. The F1 value of FeGvL was higher than that of seven advanced anomaly detection methods such as GDN (Graph Deviation Network), TranAD (Transformer-based Anomaly Detection), and GreLeN (Graph relational Learning Network), and the average F1 value was 1.7 percentage points higher than that of the second-best model GreLeN. Experimental results show that the proposed method can effectively capture spatiotemporal dependencies, provide representation capabilities, and has high anomaly detection accuracy.

Key words: anomaly detection, multivariate time series, attention mechanism, Variational auto-encoder (VAE), frequency domain enhancement