Raz Marshanski

Probabilistic time-series forecasting must capture temporal structure, express uncertainty and infer quickly; diffusion and flow-matching methods buy flexibility with multi-step iterative sampling whose cost grows with the number of steps. KGO treats prediction as the evolution of structured uncertainty in a Koopman latent space: KoPE extrapolates latent trajectories with a learned linear operator, KoFM transports noise to the future in one step via a matrix exponential, and AUG adapts the deterministic and stochastic mix per variable and horizon. Across 17 ProbTS settings, KGO reports the lowest CRPS on 13 and the lowest NMAE on 16, with at least a 25-fold inference speedup over iterative generative models.

Probabilistic time-series forecasting must capture temporal structure, express uncertainty and infer quickly; diffusion and flow-matching methods buy flexibility with multi-step iterative sampling whose cost grows with the number of steps. KGO treats prediction as the evolution of structured uncertainty in a Koopman latent space: KoPE extrapolates latent trajectories with a learned linear operator, KoFM transports noise to the future in one step via a matrix exponential, and AUG adapts the deterministic and stochastic mix per variable and horizon. Across 17 ProbTS settings, KGO reports the lowest CRPS on 13 and the lowest NMAE on 16, with at least a 25-fold inference speedup over iterative generative models.