Selective Denoising Diffusion Model for Time Series Anomaly Detection
arXiv:2602.23662v1 Announce Type: new 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
arXiv:2602.23662v1 Announce Type: new 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 AnomalyFilter achieves notably low reconstruction error on normal parts, providing empirical support for its effectiveness in anomaly detection. AnomalyFilter represents a pioneering approach that focuses on the noise design of diffusion models specifically tailored for TSAD.
Executive Summary
This article proposes AnomalyFilter, a novel diffusion-based method for Time Series Anomaly Detection (TSAD) that selectively denoises anomaly parts while retaining normal parts. By masking Gaussian noise during training and conducting denoising without adding noise, AnomalyFilter enhances the performance of naive diffusion models. The method is tested on five datasets, demonstrating low reconstruction error on normal parts and empirical support for its effectiveness in anomaly detection. This pioneering approach focuses on the noise design of diffusion models specifically tailored for TSAD, offering a promising solution for this challenging problem. The article contributes to the development of advanced TSAD methods and has significant implications for various applications in finance, healthcare, and manufacturing.
Key Points
- ▸ AnomalyFilter selectively denoises anomaly parts while retaining normal parts.
- ▸ The method is based on a novel diffusion-based approach tailored for Time Series Anomaly Detection.
- ▸ AnomalyFilter outperforms naive diffusion models in reconstruction error on normal parts.
Merits
Strength in methodological innovation
The article proposes a novel approach to TSAD, leveraging the advanced generative capabilities of diffusion models.
Empirical evaluation on various datasets
The method is tested on five datasets, providing empirical support for its effectiveness in anomaly detection.
Demerits
Limited generalizability to other domains
The article focuses on TSAD and may not be directly applicable to other anomaly detection problems.
Expert Commentary
The article presents a significant contribution to the field of TSAD, leveraging the advanced generative capabilities of diffusion models. The selective denoising approach of AnomalyFilter offers a promising solution for this challenging problem. However, the method's limitations in generalizability to other domains may be a concern. The empirical evaluation on various datasets provides strong evidence for the effectiveness of AnomalyFilter, making it a valuable addition to the TSAD literature. As the field continues to evolve, the impact of AnomalyFilter is likely to be substantial, driving innovation in anomaly detection methods and applications.
Recommendations
- ✓ Future research should explore the application of AnomalyFilter in other domains beyond TSAD, such as image or text anomaly detection.
- ✓ The method can be further improved by incorporating other techniques, such as transfer learning or domain adaptation, to enhance its generalizability.