Diffusion Model in Normal Gathering Latent Space for Time Series Anomaly Detection

Generative models have been widely used in time series anomaly detection, effectively identifying abnormal states within the data. Among these, diffusion models stand out for their powerful generative capabilities and have been increasingly applied to anomaly detection tasks, showcasing advantages in handling complex time series data. However, existing approaches employ diffusion models directly in the numerical space, which leads to several limitations, particularly in failing to reconstruct normal time series. To address these issues, we propose NGLS-Diff, an innovative approach that uses a diffusion model within a normal gathering latent space to enhance anomaly detection capabilities. This method introduces a novel latent space that captures the distributions of normal temporal patterns, thus rectifying the drawbacks of previous diffusion models. By operating the diffusion process in the normal gathering latent space, our approach significantly enhances the model’s ability to detect anomalies within normal time series data. Extensive experiments conducted on four real-world datasets demonstrate the significant performance improvements of our NGLS-Diff compared to various methods, validating its effectiveness in time series anomaly detection.