能量时间序列插补：基于裁剪感知目标条件化的差分隐私扩散模型
Energy Time-Series Imputation with Differentially Private Diffusion Models via Clipping-Aware Objective Conditioning
- Minjiang University（闽江学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于裁剪感知目标条件化的差分隐私扩散模型，用于能量时间序列插补，通过v预测和动态加权提升插补效用。
AI中文摘要：
可靠地恢复缺失测量值对于能量时间序列系统中的监测和分析至关重要，其中细粒度测量可能包含敏感的时间信息。使用差分隐私随机梯度下降（DP-SGD）训练的扩散模型为隐私敏感的能量时间序列插补提供了一个有前景的框架。在余弦扩散调度下，后期时间步对应于低信噪比（SNR）条件，此时标准的ε预测可能产生大的预裁剪梯度。这类梯度更可能被裁剪，从而减少保留的优化信号。人工智能（AI）的贡献在于将这种目标-裁剪交互表述为固定阈值DP-SGD下的目标优化问题，并开发了时间步感知的目标条件化方法用于基于扩散的能量时间序列插补。该方法采用v预测来缓解后期时间步的梯度放大，使用静态损失加权作为均匀缩放控制，并引入扩散调度感知的动态加权以在裁剪前实现更强的衰减。对于工程应用，我们在五个真实世界的能量时间序列数据集上评估了该方法，涵盖了随机点缺失、连续块缺失、持续停电和多种缺失严重程度。在匹配的DP-SGD设置下，所提出的方法在插补效用上持续优于ε预测基线。梯度诊断显示，预裁剪梯度的上尾范数更低，裁剪比例更小，并且在后期低信噪比时间步上衰减更强，支持了裁剪感知目标条件化在能量时间序列插补中的有效性。
英文摘要：
Reliable recovery of missing measurements is important for monitoring and analysis in energy time-series systems, where fine-grained measurements may contain sensitive temporal information. Diffusion models trained with differentially private stochastic gradient descent (DP-SGD) provide a promising framework for privacy-sensitive energy time-series imputation. Under cosine diffusion schedules, late timesteps correspond to low signal-to-noise ratio (SNR) conditions, where standard $\varepsilon$-prediction can induce large pre-clipping gradients. Such gradients are more likely to be clipped, reducing the retained optimization signal. The artificial intelligence (AI) contribution lies in formulating this objective--clipping interaction as an objective optimization problem under fixed-threshold DP-SGD and developing timestep-aware objective conditioning for diffusion-based energy time-series imputation. The method adopts $v$-prediction to mitigate late-timestep gradient amplification, uses static loss weighting as a uniform-scaling control, and introduces diffusion-schedule-aware dynamic weighting for stronger attenuation before clipping. For the engineering application, we evaluate the method on five real-world energy time-series datasets across random point missingness, contiguous block missingness, persistent outages, and multiple missing-data severities. Under matched DP-SGD settings, the proposed method consistently improves imputation utility over the $\varepsilon$-prediction baseline. Gradient diagnostics reveal lower upper-tail pre-clipping gradient norms, reduced clipping fractions, and stronger attenuation at late low-SNR timesteps, supporting the effectiveness of clipping-aware objective conditioning for energy time-series imputation.
