DiffTSAD: Diffusion-Guided Dynamic Graph Learning for Unsupervised Multivariate Time-Series Anomaly Detection

Multivariate time-series anomaly detection (MTSAD) is critical for monitoring complex systems, while it suffers from three key limitations: the anomaly reconstruction paradox under severe class imbalance, epoch-dependent optimization conflicts where evolving reconstruction fidelity inverts supervision signals during co-training, and rigid static graph topologies that fail to capture anomaly-induced structural shifts. We propose DiffTSAD, a framework that fundamentally decouples generative anomaly simulation from discriminative learning. By leveraging a frozen pre-trained diffusion model as a stationary perturbation prior, DiffTSAD injects severity-calibrated perturbations to establish a distribution-invariant binary classification paradigm, eliminating the non-stationary supervision of epoch-dependent pseudo-labeling methods. Diffusion-induced residuals further guide dynamic graph construction to adaptively reweight inter-variable dependencies, while a topology-aware Graph Attention Network with chunk-wise dilated temporal convolutions enables fine-grained spatio-temporal representation learning. Experiments on three real-world benchmarks demonstrate that DiffTSAD achieves state-of-the-art or competitive performance.