发表机构
University of Science and Technology of China（中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对时间序列预测中未来与历史的不对称问题，提出DiffDiff扩散框架，将可预测性不对称嵌入扩散轨迹，使前向算子依赖步骤，在多基准测试中优于基线，能集中精力于目标最不确定成分，减轻重建历史锚定内容负担。
AI 中文摘要
扩散模型已成为概率时间序列预测的广泛使用框架，用于根据观测历史对未来值分布建模。但在时间序列预测中，未来延续观测历史，造成标准扩散过程未解决的不对称性，低频内容由观测连续性决定，高频动态承载大部分剩余不确定性。现有基于扩散的预测器在生成前通过外部规则解耦这种不对称性，使损坏轨迹对历史已能锚定目标的哪些部分视而不见。我们提出DiffDiff，一种将这种可预测性不对称性嵌入扩散轨迹本身的扩散框架，使单个端到端扩散过程知晓历史已能锚定目标的哪些部分。DiffDiff使前向算子依赖步骤，让有噪声的中间状态从目标本身逐渐转向其二阶差分结构，同时一个条件路径为去噪器提供值域和差分历史信息，并在每个扩散步骤由阶段自适应门平衡。终端分布接近标准高斯分布，与现有采样器保持兼容。在四个预测范围内的七个基准测试中，DiffDiff优于六个扩散基线，我们的分析证实DiffDiff将扩散的生成努力集中在目标最不确定的成分上，同时使其无需重建历史锚定内容。
英文摘要
Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history. In time series forecasting, however, the future continues the observed history, creating an asymmetry the standard diffusion process leaves unaddressed, with slowly-varying content largely determined by the observed continuity while higher-frequency dynamics carry most of the residual uncertainty. Existing diffusion-based forecasters decouple this asymmetry through an external rule before generation, leaving the corruption trajectory blind to which parts of the target the history can already anchor. We propose DiffDiff, a diffusion framework that embeds this predictability asymmetry into the diffusion trajectory itself, so that a single end-to-end diffusion process becomes aware of which parts of the target the history can already anchor. DiffDiff makes the forward operator step-dependent so that the noisy intermediate state progressively shifts from the target itself toward its second-order differenced structure, while a conditioning pathway supplies the denoiser with both value-domain and differential history information balanced by a stage-adaptive gate at each diffusion step. The terminal distribution approaches a standard Gaussian, preserving compatibility with existing samplers. On seven benchmarks across four prediction horizons, DiffDiff outperforms six diffusion baselines, and our analysis confirms that DiffDiff concentrates the diffusion's generative effort on the most uncertain components of the target while relieving it from rebuilding the history-anchored content.
