Revisiting Noise Design in Time Series Diffusion Models: Adaptive Schedule with Temporal Prior

Diffusion models have demonstrated remarkable generative capabilities and been increasingly adopted in time series analysis. Recent research primarily focus on developing effective architectures and training protocols to improve performance on downstream tasks. Despite the critical role of noise in diffusion models, it has received comparatively limited attention, hindering time series diffusion models from fully realizing their potential. In this work, we propose a principled approach to design Adaptive Noise for Time Series diffusion models (ANTS) based on the inherent and unique properties of data. Our core insight is that an efficient noise schedule should distribute corruption process to maintain uniform information degradation across diffusion steps, eliminating redundant noise levels that contribute little learnable signal. It improves the precision of noise estimation and facilitates better traceability of diffusion. Furthermore, we incorporate informative statistic priors to capture temporal dependencies and enhance sample fidelity during reverse process. Our method incurs only marginal additional offline computational cost and can be seamlessly integrated into frameworks based on standard diffusion processes. Experiments on generation and forecasting tasks across various datasets demonstrate that ANTS improves generative quality and predictive performance over baselines, validating its effectiveness for time series diffusion models.