StrideDiffusion: Accelerating Diffusion Models for Time-Series Generation

StrideDiffusion accelerates time-series diffusion without retraining by using spectral activity to choose denoising times. The sampler tracks band power and changes in power and phase, uses a history correction on eligible jumps, and returns to fine steps near the clean endpoint. We test whether its gains come from grid placement rather than the update rule alone. On six datasets, we select configurations on validation data and compare them on separate data using three independently trained models per dataset. A uniform-grid replay matches every batch’s denoiser call count and the choice between a history correction and a deterministic DDIM update at each update index. The spectral grid has lower mean Context-FID, a feature-based distribution distance, on five datasets, with ETTh as the exception. The complete sampler also has lower mean Context-FID than DPM-Solver++ on five datasets and UniPC on all six at approximately matched call budgets, and is faster than both in synchronized sampling measurements. These gains do not extend to every quality metric. Our analysis separates DDIM displacement from the history residual and gives a local sufficient condition for DDIM. The implemented activity gate does not certify that condition. Experiments on additional backbones test spectral scheduling in other parameterizations. The results support spectral grid selection with a history update, while leaving the necessity of online adaptation open. Code repository: https://anonymous.4open.science/r/stridediff-ts