Robust time series generation via Schrödinger Bridge: a comprehensive evaluation

Alexandre Alouadi, Baptiste Barreau, Laurent Carlier, Huyen PHAM

Abstract:

We investigate the generative capabilities of the Schrödinger Bridge
(SB) approach for time series. The SB framework formulates time
series synthesis as an entropic optimal interpolation transport prob-
lem between a reference probability measure on path space and
a target joint distribution. This results in a stochastic differential
equation over a finite horizon that accurately captures the tem-
poral dynamics of the target time series. While the SB approach
has been largely explored in fields like image generation, there is a
scarcity of studies for its application to time series. In this work, we
bridge this gap by conducting a comprehensive evaluation of the SB
method’s robustness and generative performance. We benchmark
it against state-of-the-art (SOTA) time series generation methods
across diverse datasets, assessing its strengths, limitations, and
ability to model complex temporal dependencies. Our results offer
valuable insights into the SB framework’s potential as a versatile
and robust tool for time series generation.