Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various tasks. However, prior works on TS diffusion models often borrow the framework of existing works proposed in other domains without considering the characteristics of TS data, leading to suboptimal performance. In this work, we propose Adaptive Noise schedule for Time series diffusion models (ANT), which automatically predetermines proper noise schedules for given TS datasets based on their statistics representing non-sResearch goal: Can the adaptive noise scheduling in ANT be extended to other generative models (e.g., VAE, GAN) for time series data, and how does it affect their efficiency (measured by convergence speed) and sample quality (measured by IS/FID) on the Monash benchmark?Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.