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arXiv:2610.32329v1 Announce Type: new Abstract: Spatiotemporal forecasting plays an important role in real-world applications, where probabilistic approaches provide uncertainty estimates for decisio...
The paper compares two main strategies for producing probabilistic forecasts of physical systems: generative models (e.g., diffusion or flow matching) and ensembles of deterministic models trained with the continuous ranked probability score (CRPS). It introduces a framework that evaluates both methods on diverse 2‑D spatiotemporal systems, measuring empirical coverage of predictive intervals, accuracy, and computational efficiency. The study finds that CRPS‑trained ensembles generally provide more reliable uncertainties and faster inference, while generative models trained in ambient space can match coverage but at higher latency. The authors also release AutoCast, a modular framework for both approaches, and AutoSim, a dataset generation tool.
arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.
arXiv:2607. 00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise.
arXiv:2603. 07108v2 Announce Type: replace-cross Abstract: Accurate and reliable forecasting of epidemic incidences is critical for public health preparedness, yet it remains a challenging task due to complex nonlinear temporal dependencies and heterogeneous spatial interactions.
The paper presents a probabilistic post‑processing framework that uses a conditional Variational Autoencoder (cVAE) with a non‑parametric decoder to adjust seasonal Arctic sea‑ice forecasts. By replacing the Gaussian decoder with a generator and optimizing with Continuous Ranked Probability Score (CRPS), the method produces larger, bias‑corrected ensembles that are better calibrated, more consistent with observations, and exhibit higher resolution and sharper spectral power than standard cVAEs. The approach demonstrates improved forecast skill and reduced errors compared to benchmark predictions.