A Correlation-Aware Diffusion Model for Multivariate Time Series Anomaly Detection with Missing Values

Incomplete time series data is a common problem in real-world application scenarios. Recent research has taken the approach of separating interpolation and anomaly detection, which is not interactive and performs poorly. On the other hand, interpolation using traditional methods relies on a large amount of a priori knowledge, and using deep learning methods takes up a large amount of computational resources and is inefficient. In this study, we propose a correlation-aware diffusion model that successfully bypasses the above problems. Our approach focuses on capturing deep multivariate correlations from limited incomplete data and use low-frequency component to guide generation. Experiments on four realistic scenario datasets covering three domains show that our method achieves better anomaly detection results than existing methods for various missing rates.