arXiv：cs.LG（机器学习，全量分类）· Huizhen Huang, Yu Li, Tao Huang, Chen Hou·· 14 小时前AI 评分24
基于差分隐私扩散模型的能源时间序列插补：裁剪感知目标条件化
Energy Time-Series Imputation with Differentially Private Diffusion Models via Clipping-Aware Objective Conditioning
AI 导读
研究提出一种裁剪感知目标条件化方法，用于差分隐私扩散模型的能源时间序列插补，通过采用 v-prediction 缓解晚期低 SNR 时间步的梯度放大，并引入扩散调度感知的动态加权在裁剪前实现更强衰减。在五个真实能源时间序列数据集上，该方法在随机点缺失、连续块缺失、持续中断及多种缺失严重程度下，插补效用均优于 ε-prediction 基线。梯度诊断显示裁剪前梯度范数上尾更低、裁剪比例下降。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00209 [cs.LG] |
| (or arXiv:2610.00209v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00209 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tao Huang [view email]
[v1]
Mon, 21 Sep 2026 03:37:02 UTC (109 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org