AI 中文总结
FirstDiff是一种基于初始噪声预测的单步扩散异常检测框架，采用Diffusion Transformer作为骨干，仅需一次去噪网络评估即可在五个基准数据集上实现最优多变量时间序列异常检测性能。
AI 中文摘要
扩散模型近期通过迭代去噪学习正常数据分布，在多变量时间序列异常检测中展现出强大潜力。然而现有基于扩散的方法通常在完成反向扩散过程后才进行异常检测，主要依赖最终重构信号，忽略了去噪过程中产生的有效表示。这种设计带来了巨大的计算成本，且限制了中间扩散信息用于异常检测。在本文中，我们提出FirstDiff，一种基于扩散的异常检测框架，其核心观察是：初始反向扩散评估时预测的扩散噪声已包含足够信息用于准确的异常检测。FirstDiff利用验证数据对正常行为下预测扩散噪声的统计分布进行建模，从而无需完成整个反向扩散轨迹，仅通过一次去噪网络评估即可进行异常推断。为建模复杂的时间和传感器间依赖关系，FirstDiff采用Diffusion Transformer作为去噪骨干网络。在五个公共基准数据集上的大量实验表明，FirstDiff实现了最优性能，同时将扩散推理从完整反向轨迹减少为单次去噪网络评估。
英文摘要
Diffusion models have recently shown strong potential for multivariate time-series anomaly detection by learning the distribution of normal data through iterative denoising. Existing diffusion-based approaches, however, typically perform anomaly detection after completing the reverse diffusion process, relying primarily on the final reconstructed signal and overlooking informative representations produced during denoising. This design incurs substantial computational cost and limits the use of intermediate diffusion information for anomaly detection. In this paper, we propose FirstDiff, a diffusion-based anomaly detection framework based on the observation that the predicted diffusion noise at the initial reverse-diffusion evaluation already contains sufficient information for accurate anomaly detection. FirstDiff models the statistical distribution of predicted diffusion noise under normal behavior using validation data, enabling anomaly inference from a single denoising-network evaluation rather than completing the reverse diffusion trajectory. To model complex temporal and inter-sensor dependencies, FirstDiff employs a Diffusion Transformer as the denoising backbone. Extensive experiments on five public benchmark datasets demonstrate that FirstDiff achieves state-of-the-art performance while reducing diffusion inference from the full reverse trajectory to a single denoising-network evaluation.
