KGO pairs Koopman dynamics with single-step flow matching for fast probabilistic forecasting
SequentialLearning · hf · 2026-11-05
Researchers from Ben-Gurion University and Amazon Science propose the Koopman Generative Operator (KGO) for efficient probabilistic time-series forecasting.
- Motivation: existing methods struggle to combine structured temporal dynamics, expressive uncertainty, and efficient inference — latent dynamical models are restrictive, while diffusion/flow matching are flexible but sampling is slow and costly
- Components: Koopman Patch Embedding (KoPE) for temporally consistent latent trajectories, structured latent dynamics with single-step flow matching, and adaptive uncertainty gating
- Frames prediction as the evolution of structured uncertainty, balancing accuracy and inference efficiency
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