SCTF: Self-Correcting Time-Frequency Diffusion for Multivariate Time-Series Anomaly Detection

Recently, diffusion models have shown considerable promise for multivariate time-series anomaly detection by reconstructing masked observations from the remaining context and using reconstruction errors as anomaly evidence. However, anomalies in the visible context can bias the reconstruction toward abnormal patterns, making the reconstruction appear more similar to the anomalous observations and thereby making the anomalies harder to distinguish. Existing strategies attempt to limit the influence of unreliable conditioning contexts, but such control relies on reliability estimates that may themselves be inaccurate and are often treated as fixed during reconstruction. This reveals a broader bottleneck: effective anomaly reconstruction depends not only on reconstruction capability, but also on whether conditioning reliability can be accurately estimated, controlled, and corrected. To address this bottleneck, we introduce SCTF (Self-Correcting Time-Frequency Diffusion), built on the principle that conditioning reliability should be treated as a dynamic quantity rather than a fixed judgment. SCTF estimates context risks from complementary views, uses them to adaptively reduce the influence of potentially unreliable contexts, and progressively refines the risks using reconstruction feedback, forming a self-correcting loop between reliability estimation and generative reconstruction. Synchronized temporal and frequency diffusion further allows frequency-domain reconstruction to provide auxiliary information for temporal denoising while offering complementary feedback for risk refinement. Extensive experiments on benchmark and real-world datasets demonstrate strong F1 performance against representative state-of-the-art methods.