不用跑完整扩散过程，第一次预测噪声就能判断异常，速度快还更准，时序异常检测可以看看这个方法。
FirstDiff 提出基于初始噪声预测的异常检测框架，只需一次去噪网络评估即可完成推理，无需走完整个反向扩散轨迹。它利用验证数据对正常行为下的预测扩散噪声进行统计建模，从而从单次评估中推断异常。FirstDiff 采用 Diffusion Transformer 作为去噪骨干，以建模复杂的时间与传感器间依赖。在五个公开基准数据集上，FirstDiff 达到最先进性能，同时将扩散推理从完整反向轨迹缩减为单次去噪评估。
FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction
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.