The paper introduces Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting, showing that integrating predicted quantiles as distributional controls can guide a pretrained diffusion forecaster. The authors find that applying residual control throughout the denoising network yields the most consistent improvements, leading to the Control-TS model that learns a quantile-control pathway while keeping the backbone frozen. Across eight benchmarks, Control-TS improves the backbone’s CRPS, competes with state‑of‑the‑art models, and retains gains in capacity‑matched comparisons, with further analyses on quantile granularity, predictor choice, and forecast quality. The study also presents the Conditional Linear Predictive Score (CLPS) to evaluate downstream forecasting utility of generated futures.
By Shadi Omari, Bashar Khoury, Assaf Schuster
arXiv:2605. 30122v2 Announce Type: replace-cross Abstract: Deep-learning precipitation nowcasting models are often optimized using pointwise losses such as mean squared error or mean absolute error, which can lead to overly smooth forecasts and poor representation of heavy rainfall.
By Gijs van Nieuwkoop, Siamak Mehrkanoon
arXiv:2610.10076v1 Announce Type: cross
Abstract: Calibration is an essential requirement for probabilistic predictions to be useful for decision making. While state-of-the-art prediction methods oft...
By Jakob Benjamin Wessel, Sam Allen
arXiv:2608. 15290v1 Announce Type: cross Abstract: The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction.
By Mandy Yao (University of Toronto), Meredith Franklin (University of Toronto)
arXiv:2608. 16864v1 Announce Type: cross Abstract: In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number.
By Shuai Huang, Zhe Qu, Zhaowei Hua, Guohao Shen, Rui Tang, Hongtu Zhu
arXiv:2607. 26792v1 Announce Type: cross Abstract: Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC).
By Runyao Yu, Yuchen Tao, Yujie Chen, Wentao Wang, Derek W. Bunn