Conditional Flow Matching for Time Series Modelling
Abstract
Learning dynamical systems from long trajectories is a challenging problem due to the complexity of the loss landscape. Inspired by conditional flow matching in generative modelling, we propose a new approach for training neural ODEs based on regressing vector fields of conditional probability paths defined per trajectory. Our Conditional Flow Matching for Time Series (CFM-TS) objective outperforms neural ODEs trained with the adjoint method on three simulated tasks, including a pendulum system where the neural ODE does not converge at all.
Cite
Text
Tamir et al. "Conditional Flow Matching for Time Series Modelling." ICML 2024 Workshops: SPIGM, 2024.Markdown
[Tamir et al. "Conditional Flow Matching for Time Series Modelling." ICML 2024 Workshops: SPIGM, 2024.](https://mlanthology.org/icmlw/2024/tamir2024icmlw-conditional/)BibTeX
@inproceedings{tamir2024icmlw-conditional,
title = {{Conditional Flow Matching for Time Series Modelling}},
author = {Tamir, Ella and Laabid, Najwa and Heinonen, Markus and Garg, Vikas and Solin, Arno},
booktitle = {ICML 2024 Workshops: SPIGM},
year = {2024},
url = {https://mlanthology.org/icmlw/2024/tamir2024icmlw-conditional/}
}