[2602.23662] Selective Denoising Diffusion Model for Time Series Anomaly Detection

Computer Science > Machine Learning arXiv:2602.23662 (cs) [Submitted on 27 Feb 2026] Title:Selective Denoising Diffusion Model for Time Series Anomaly Detection Authors:Kohei Obata, Zheng Chen, Yasuko Matsubara, Lingwei Zhu, Yasushi Sakurai View a PDF of the paper titled Selective Denoising Diffusion Model for Time Series Anomaly Detection, by Kohei Obata and 4 other authors View PDF HTML (experimental) Abstract:Time series anomaly detection (TSAD) has been an important area of research for decades, with reconstruction-based methods, mostly based on generative models, gaining popularity and demonstrating success. Diffusion models have recently attracted attention due to their advanced generative capabilities. Existing diffusion-based methods for TSAD rely on a conditional strategy, which reconstructs input instances from white noise with the aid of the conditioner. However, this poses challenges in accurately reconstructing the normal parts, resulting in suboptimal detection performance. In response, we propose a novel diffusion-based method, named AnomalyFilter, which acts as a selective filter that only denoises anomaly parts in the instance while retaining normal parts. To build such a filter, we mask Gaussian noise during the training phase and conduct the denoising process without adding noise to the instances. The synergy of the two simple components greatly enhances the performance of naive diffusion models. Extensive experiments on five datasets demonstrate that An...