Leveraging Priors via Diffusion Bridge for Time Series Generation
Standard Gaussian diffusion priors struggle to capture temporal structures, scale sensitivity, and fixed-point constraints inherent in time series. To address this, we propose TimeBridge—a novel framework that systematically introduces data- and time-dependent priors alongside scale-preserving constraint priors, enabling a learnable diffusion bridge mechanism for probabilistic transport from adaptive priors to the target data distribution. TimeBridge unifies unconditional and conditional generation, offering both flexibility and precise controllability. Evaluated on multiple benchmark time-series datasets, it achieves state-of-the-art performance in generation diversity, fidelity, and temporal consistency—significantly outperforming existing diffusion-based baselines.