用自监督编码生成时间序列补全，同时提供可靠置信区间。
SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting
- 先用JEPA编码器压缩负荷数据，再用扩散模型生成缺失轨迹。
- 在13个数据集上误差最低，91天缺失时仍优于5个主流方法。
- 首次实现在线自适应置信带，覆盖率达93%-95%，适合电力系统决策使用。
时间序列补全的生成模型虽有高重建精度，但缺乏有限样本可靠性保障，这在电力系统调度与规划中尤为关键。本文提出SPLICE（自监督预测潜空间补全与置信包络），将潜空间生成补全与无分布、在线自适应预测区间结合。一个JEPA编码器将日负荷片段映射至64维潜空间；带有四种采样模式的条件潜空间桥梁生成候选缺失轨迹；小时条件解码器将其还原至信号空间；自适应共形推断（ACI）为输出添加具有覆盖率保证的预测带。流匹配变体仅需5-10步常微分方程求解，速度提升5-10倍，性能媲美DDIM。在十三个负荷数据集（九个专有，三个UCI Electricity，ETTh1）上，SPLICE实现最低的负载仅均方误差（0.056），在91天缺失情况下胜出9/12非退化数据集，所有缺失长度下胜出18/32，且达到最佳连续概率评分（CRPS=0.161，较最强对手降低18.3%）。ACI实现93%-95%经验覆盖率，纠正了静态共形预测最高达7.5个百分点的覆盖不足问题。在九个馈线数据上联合训练的JEPA编码器可迁移至四个未见领域，仅需快速桥接微调即达到或超过单数据集最优基准。
原文摘要 · Abstract (English)
Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical limitation in power systems where imputed values inform dispatch and planning. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes), a modular framework coupling latent generative imputation with distribution-free, online-adaptive prediction intervals. A JEPA encoder maps daily load segments into a 64-dimensional latent space; a conditional latent bridge with four sampling modes generates candidate gap trajectories; an hourly-conditioned decoder maps back to signal space; and Adaptive Conformal Inference (ACI) wraps the output with coverage-guaranteed prediction bands. The flow-matching variant achieves comparable quality to DDIM in 5--10 ODE steps (5-10x speedup). On thirteen load datasets (nine proprietary, three UCI Electricity, ETTh1), SPLICE achieves the lowest mean Load-only MSE (0.056), winning 9/12 non-degenerate datasets at 91-day gaps and 18/32 across all gap lengths vs. five established baselines, and produces the best CRPS (0.161, -18.3% vs. the strongest competitor). ACI delivers 93--95% empirical coverage, correcting under-coverage failures of up to 7.5 pp observed with static conformal prediction. A pooled JEPA encoder trained on nine feeds transfers to four unseen domains, matching or exceeding per-dataset oracles with only a quick bridge fine-tuning.
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