用生成对抗网络增强扩散模型，提升多变量时间序列异常检测效果
Multivariate Time Series Anomaly Detection using DiffGAN Model
- 在扩散模型去噪器中加入GAN，同步生成噪声数据并预测扩散步数
- 在多个基准数据集上优于现有主流重建模型的异常检测性能
- 适合需要高精度时序异常识别的工业监控与金融风控场景
近年来，部分研究者将扩散模型应用于多变量时间序列异常检测。通常采用依赖扩散步数的局部扩散策略进行异常检测，但不同扩散步数会影响原始数据的重建质量，进而影响检测效果。为解决该问题，本文提出一种名为DiffGAN的新方法，通过在扩散模型的去噪器中引入生成对抗网络组件，实现噪声数据的同步生成与扩散步数的预测。实验结果表明，相较于多种先进重建模型，DiffGAN在异常检测任务中表现出更优性能。
原文摘要 · Abstract (English)
In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection. The partial diffusion strategy, which depends on the diffusion steps, is commonly used for anomaly detection in these models. However, different diffusion steps have an impact on the reconstruction of the original data, thereby impacting the effectiveness of anomaly detection. To address this issue, we propose a novel method named DiffGAN, which adds a generative adversarial network component to the denoiser of diffusion model. This addition allows for the simultaneous generation of noisy data and prediction of diffusion steps. Compared to multiple state-of-the-art reconstruction models, experimental results demonstrate that DiffGAN achieves superior performance in anomaly detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。