Modeling Temporal Data as Continuous Functions with Process Diffusion

A diffusion-based generative model is developed that adds noise from a predefined stochastic process while preserving the continuity of the resulting underlying function and a neural network is trained to reverse this process which allows to sample new realizations from the learned distribution.