Probabilistic Forecasting with Stochastic Interpolants and F\"ollmer Processes

arXiv:2403.13724v1 Announce Type: new
Abstract: We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasting problem as sampling from the conditional distribution of the future system state given its current state. To this end, we leverage the framework of stochastic interpolants, which facilitates the construction of a generative model between an arbitrary base distribution and the target. We design a fictitious, non-physical stochastic dynamics …

abstract arxiv cs.lg current distribution forecasting framework future generative generative modeling modeling processes sampling state stat.ml stochastic systems type