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#machine learning Preprint May 2025

A Jump-Diffusion Framework for Irregular Time Series Generation

The key ingredient is that, for a 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.

O. Pfohl, J. Chemseddine, P. Hagemann et al. · 1 citation

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