arXiv:2607. 16238v1 Announce Type: cross Abstract: Predicting droplet evolution in material jetting, or Inkjet Printing (IJP), is essential for maintaining printing quality.
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DiffPTS rethinks the evidence lower bound (ELBO) for diffusion-based probabilistic time‑series forecasting by adopting a Location‑Scale Noise Model, which yields a Gaussian negative log‑likelihood objective and a unified joint training objective. The framework enables end‑to‑end optimization of all components within the ELBO, unlike prior methods that treat estimator training as separate regression tasks. On multiple benchmarks, DiffPTS achieves state‑of‑the‑art performance, reducing CRPS/MSE by over 14.53%/16.55% compared to existing diffusion‑based models.
arXiv:2606.21036v2 Announce Type: replace-cross Abstract: In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approa...
arXiv:2607. 17972v1 Announce Type: new Abstract: The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration.
Just Weather Scoring (JWS) is a single‑stage, end‑to‑end diffusion model that forecasts precipitation directly in radar space, eliminating the need for separate compression or deterministic components. By combining Masked Asynchronous Diffusion with a scoring‑rule objective, JWS adapts diffusion training to high‑dimensional spatio‑temporal data and enables few‑step generation. On SEVIR and MeteoNet benchmarks, JWS achieves state‑of‑the‑art probabilistic forecasting while using fewer parameters and delivering over 17× faster inference than existing methods.
The paper introduces a new diffusion-based approach for probabilistic weather forecasting that incorporates auxiliary conditional denoising tasks. By predicting future states from both clean and corrupted contexts, the model reduces ambiguity and learns conditional distributions across noise levels using a proper scoring rule. Experiments demonstrate improved one‑step forecasting, especially at longer horizons, and better calibration in high‑dimensional global weather scenarios.
arXiv:2607. 22599v1 Announce Type: new Abstract: Diffusion models have become a widely used framework for probabilistic time series forecasting, modeling the distribution of future values given an observed history.