Nonparametric generative modeling for time series via Schr{\"{o}}dinger bridge

Researchers propose a generative model for time series (sequences of data points over time) based on Schrödinger bridge, a mathematical technique that uses optimal transport (finding the most efficient way to transform one data distribution into another) to create synthetic time series data. The model estimates unknown functions from real data using nonparametric methods (techniques that don't assume a specific underlying mathematical form) and generates new synthetic samples that preserve the temporal patterns in the original data.