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arXiv · 2505.23215

A Jump-Diffusion Framework for Irregular Time Series Generation

O. Pfohl·J. Chemseddine·P. Hagemann·G. Steidl·C. Wald·T. Jahn
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Abstract

We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on the matching of generators and accommodates discontinuous trajectories. Analytical formulas for diffusion and jump bridges yield a family of reference generators that a neural network is trained to match. The key ingredient is that, for our constructed jump bridge, a parametrization of the jump kernel densities by scaled Gaussians admits closed-form expressions for the Kullback-Leibler divergence, allowing simulation-free training.

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O. Pfohl, J. Chemseddine, P. Hagemann, G. Steidl, C. Wald, T. Jahn. 2025-05-29. A Jump-Diffusion Framework for Irregular Time Series Generation. https://arxiv.org/abs/2505.23215

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