发表机构
UNSW; RMIT; Aalto University; HKUST(GZ)（新南威尔士大学; 皇家墨尔本理工大学; 阿尔托大学; 香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对扩散模型推理时去噪步骤多限制实际应用的问题，提出StrideDiffusion采样器，依据频带活动自适应选步长，经实验验证该方法能大幅加速时间序列生成，保持或提升质量，为快速采样提供实用信号。
AI 中文摘要
扩散模型已成为时间序列的有竞争力的生成器，但推理时所需的大量顺序去噪步骤限制了其实际应用。现有快速采样器通常使用固定或通用时间步长调度，忽略了时间序列扩散的一个独特属性：反向过程中不同频谱带以不同速率演变。我们引入了StrideDiffusion，一种无需训练的频谱感知采样器，它根据频带级活动自适应选择去噪步长。在每个步骤中，StrideDiffusion监测相对频带能量、对数功率漂移和相速度，以确定高频动态是否仍活跃或轨迹是否由稳定的低频结构主导。然后，当快速变化的频带活跃时采取精细步骤，一旦只剩下粗糙成分就进行更大的跳跃。频带稳定性分析表明，在确定性仿射反向更新下，不活跃频带仅随跳跃大小线性变化，为将频谱活动作为步长指标提供了局部依据。在六个无条件时间序列生成基准测试中，StrideDiffusion仅使用14 - 66次函数评估而非500/1000次去噪步骤，在保持或提高生成质量的同时实现了高达18.9倍的时钟加速。在条件插补和预测方面，它在可比预测精度下进一步实现了5 - 14倍的平均加速。这些结果表明，频谱演变可为快速时间序列扩散采样提供实用且有原则的信号。我们的代码可在这个https网址获取。
英文摘要
Diffusion models have become competitive generators for time series, but their practical use is limited by the large number of sequential denoising steps required at inference time. Existing fast samplers typically use fixed or generic timestep schedules, overlooking a distinctive property of time-series diffusion: different spectral bands evolve at different rates during the reverse process. We introduce StrideDiffusion, a training-free spectral-aware sampler that adaptively selects the denoising stride from band-level activity. At each step, StrideDiffusion monitors relative band energy, log-power drift, and phase velocity to identify whether high- frequency dynamics remain active or whether the trajectory is dominated by stable low-frequency structure. It then takes fine steps when rapidly varying bands are active and larger jumps once only coarse components remain. A bandwise stability analysis shows that inactive frequency bands change only linearly with the jump size under deterministic affine reverse updates, providing a local justification for spectral activity as a step-size indicator. Across six unconditional time-series generation benchmarks, StrideDiffusion uses only 14-66 function evaluations instead of 500/1000 denoising steps, achieving up to 18.9x wall-clock speedup while preserving or improving generation quality. On conditional imputation and forecasting, it further delivers 5-14x average acceleration with comparable predictive accuracy. These results show that spectral evolution provides a practical and principled signal for fast time-series diffusion sampling. Our code is available at https://anonymous.4open.science/r/stridediff-ts.
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