使用DiffGAN模型的多元时间序列异常检测
Multivariate Time Series Anomaly Detection using DiffGAN Model
AI总结：
针对扩散模型中不同扩散步数影响多元时间序列异常检测效果的问题，本文提出DiffGAN方法，通过在去噪器中引入生成对抗网络同时生成噪声数据并预测扩散步数，实验表明其异常检测性能优于多个最先进的重建模型。
摘要：
近年来，一些研究者已将扩散模型应用于多元时间序列异常检测。在这些模型中，依赖于扩散步数的部分扩散策略常被用于异常检测。然而，不同的扩散步数会影响原始数据的重建，从而影响异常检测的效果。为解决这一问题，我们提出了一种名为DiffGAN的新方法，该方法在扩散模型的去噪器中加入了生成对抗网络组件。这一改进使得模型能够同时生成含噪数据并预测扩散步数。与多个最先进的重建模型相比，实验结果表明DiffGAN在异常检测中取得了更优越的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
