WaveGraphFormer: A Unified Framework of Dynamic Graph Learning and Multi-Scale Wavelet Transform for Multivariate Time Series Anomaly Detection

With the widespread deployment of industrial systems and Internet of Things (IoT) devices, multivariate time series anomaly detection has become increasingly important for ensuring system reliability and operational safety. However, accurately detecting anomalies in complex industrial environments remains challenging because existing approaches often fail to jointly model evolving inter-variable dependencies and multi-scale temporal patterns. To address these challenges, this paper proposes WaveGraphFormer (WGF), a unified framework for multivariate time series anomaly detection. The proposed method introduces a lightweight dynamic graph learning module to capture time-varying dependencies among variables and employs discrete wavelet transform (DWT) to extract multi-scale temporal-frequency features. In addition, a graph-guided residual modulation mechanism is designed to facilitate joint spatio-temporal representation learning. Experiments conducted on five public benchmark datasets (SWaT, WADI, SMAP, SMD, and MSL) demonstrate that WGF consistently achieves competitive performance in terms of F1-score and AUC compared with several state-of-the-art baselines. Ablation studies further validate the effectiveness of each component in the proposed framework. These results highlight the potential of WGF to provide reliable anomaly detection for complex industrial monitoring systems and establish a foundation for future research on adaptive spatio-temporal-frequency modeling.