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
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
By Kun Jin, James Harrison, Jiawei Li, Sihan Liu, Jiayi Liu, Randolph Linderman, Yuening Li, Arnab Bhadury, Sourabh Prakash Bansod, Liang Liu, Jasper Snoek
Occupancy-based Quantile Risk Control (OQRC) is a new method that extends conformal risk control to quantile-based risk measures. It partitions the loss space using ordered calibration losses, estimates the distribution of test losses in each bin, and upper-bounds the risk by the maximum loss per bin. The approach guarantees finite-sample validity, achieving tight risk control bounds that converge at a rate of σ(n^{-1/2}) and reducing the risk gap by up to 78.64% in experiments.
By Zihao Shi, Huajun Xi, Bingyi Jing, Hongxin Wei
arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
By Ilia Azizi, Juraj Bodik, Jakob Heiss, Bin Yu
arXiv:2606. 03245v1 Announce Type: cross Abstract: Concepts of calibration formalize the compatibility between probabilistic predictions and the respective outcomes.
By Johannes Resin, Lu Yang, Tilmann Gneiting
arXiv:2607. 11653v1 Announce Type: new Abstract: Black-box conditional quantile forecasts are widely used for sequential decisions under asymmetric costs, such as inventory planning in supply chain management.
By Ivane Antonov, Sohom Mukherjee, Richard Pibernik, Yo Joong Choe
LEAP (Likelihood Elicitation and Aggregation for Probabilistic forecasting) is a new approach that reorganizes how evidence is used in LLM-based forecasting systems. Instead of a monolithic prediction that aggregates all evidence at once, LEAP examines each evidence item separately, elicits likelihood parameters, and combines them with an explicit prior to produce a posterior distribution. The method supports continuous, single-choice, and multi-choice forecasts and has been shown to improve prediction and calibration metrics across models on a benchmark covering forecasting, information-seeking, and browsing tasks.
By Yufei Chen, Yiran Zhao, Xiaogang Xu, Qipeng Xie, Jiafei Wu, Zhe Liu