This paper proposes a constrained posterior sampling approach for time series generation with hard constraints.

We tackle the challenge of few-shot time series generation by proposing a unified pretrained model that outperforms state-of-the-art baselines across diverse domains.

A novel framework for generating regular time series from irregularly-sampled data using a Time Series Transformer and vision diffusion model with masking, achieving state-of-the-art performance and efficiency.