Spatio-Temporal Weighted Graph Reason Learning for Multivariate Time-Series Anomaly Detection

Constructing an efficient and deployable anomaly detection system requires achieving high accuracy, low latency, and reliability. Existing methods either spend considerable time extracting rich spatio-temporal features to enhance anomaly detection performance, or blindly integrate multi-source features to boost accuracy, often neglecting the reliability of feature aggregation. The trade-off between the three objectives must be carefully considered when developing the model. To address these challenges, we introduce a novel Spatio-Temporal Weighted Graph Reasoning Learning (STWGRL) framework for multivariate time-series anomaly detection. Specifically, we propose a series-denoising receptance-weighted key value (D-RWKV) module to efficiently capture and model expressive long-term sequence information through a linear scaling mechanism. D-RWKV ensures compatibility by alleviating the memory bottleneck and enabling parallelized training. Furthermore, we design a targeted-awareness graph adaptive aggregation (TaGAA) module to learn the directed graph and adaptively enhance the signal’s intrinsic characteristics. Two graph-constraint losses are employed to strengthen the consistency and sparsity of the learned graphs. Experimental results on multiple benchmark tasks clearly demonstrate the effectiveness of the proposed framework. STWGRL achieves more accurate scores than most baselines, while containing fewer than 10K parameters.