Koopman Generative Operators advance probabilistic time-series forecasting
Original title: Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting
The Koopman Generative Operator achieves state-of-the-art accuracy on 13/17 ProbTS benchmarks with at least 25 times faster inference than iterative generative models.
The method integrates Koopman Patch Embedding, Koopman Flow Matching and an Adaptive Uncertainty Gate for probabilistic forecasting. It unifies structured temporal dynamics with expressive uncertainty and fast single-step generation. The framework advances the accuracy-efficiency trade-off in probabilistic time-series forecasting.
- It achieves state-of-the-art accuracy on 13/17 ProbTS benchmarks in CRPS and 16/17 in NMAE.
- It delivers at least a 25 times reduction in inference time compared to iterative generative models.
From the original article
1 min read at huggingface.co
Probabilistic time-series forecasting requires models that simultaneously capture structured temporal dynamics, expressive uncertainty, and efficient inference, yet existing approaches fall short of this goal: latent dynamical models impose structure but rely on restrictive generation mechanisms, while modern generative methods such as diffusion and flow matching achieve flexibility at the cost of iterative and…
By Raz Marshanski, Liran Nochumsohn, Mayank Jauhari · huggingface.co
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