标题： 使用 DiffGAN 模型的多变量时间序列异常检测 显示英文标题

标题： Multivariate Time Series Anomaly Detection using DiffGAN Model

摘要： 近年来，一些研究者将扩散模型应用于多变量时间序列异常检测。这些模型通常采用依赖于扩散步数的部分扩散策略来进行异常检测。然而，不同的扩散步数会对原始数据的重构产生影响，从而影响异常检测的有效性。为了解决这一问题，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。这种添加使得可以同时生成噪声数据和预测扩散步数。与多个最先进的重构模型相比，实验结果表明 DiffGAN 在异常检测方面表现出色。 显示英文摘要

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.