AnoGrad: Time Series Anomaly Detection with Score-Based Generative Model

Time series anomaly detection is an extensively researched yet challenging task in modern academic studies. Previous studies still experience performance degradation caused by anomaly concentration. To address this challenge, we propose a framework combining Transformer and score-based generative models for time series anomaly detection, named AnoGrad. The model employs an innovative Trans-former model embedded with a variational AutoEncoder to capture temporal dependencies and distribution information. For anomaly concentration issues, we design a novel conditional stochastic differential equation based on score matching and use a new Variance Hybrid process to perturb the probability density space uniformly and controllably, improving adaptation to concentrated anomalies in diverse scenarios. Besides, we design a score estimation network that takes temporal and distributional features as conditional inputs to reduce uncertainty in the time series data generation process. Extensive experiments on multiple real-world datasets show that AnoGrad achieves competitive performance, often rivaling state-of-the-art benchmarks.