Jump-Diffusion Framework

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papersSEP 10 04:00 UTC

Jump-Diffusion Framework Introduced for Generating Irregularly Sampled Time Series

A research paper presents a method for training generative models on continuous-time data that is recorded unevenly and out of sync across sources. The approach builds on generator matching and can represent trajectories with sudden jumps rather than only smooth paths, backed by closed-form expressions for diffusion components. It could be useful in domains where measurements arrive at irregular intervals, such as healthcare monitoring or sensor networks.

time-series/generative-models/diffusion/research