🤖 AI Summary
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.
📝 Abstract
Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis test techniques. Recently, diffusion models have emerged as the de facto approach for time series generation, emphasizing diverse synthesis scenarios based on historical or correlated time series data streams. Since time series have unique characteristics, such as fixed time order and data scaling, standard Gaussian prior might be ill-suited for general time series generation. In this paper, we exploit the usage of diverse prior distributions for synthesis. Then, we propose TimeBridge, a framework that enables flexible synthesis by leveraging diffusion bridges to learn the transport between chosen prior and data distributions. Our model covers a wide range of scenarios in time series diffusion models, which leverages (i) data- and time-dependent priors for unconditional synthesis, and (ii) data-scale preserving synthesis with a constraint as a prior for conditional generation. Experimentally, our model achieves state-of-the-art performance in both unconditional and conditional time series generation tasks.