Robust time series generation via Schrödinger Bridge: a comprehensive evaluation
This work addresses the need for robust time series generation methods, particularly for modeling complex temporal dependencies, but it is incremental as it applies an existing SB framework to a new domain.
The paper tackled the problem of generating time series using the Schrödinger Bridge (SB) approach, which had been underexplored for this application, and found that it offers robust performance and accurately captures temporal dynamics, though specific numerical results were not provided.
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 problem 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 temporal 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 capacity 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.