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OrderFlowDiT提出了一种带非平衡最优传输正则的条件扩散Transformer，用于预测未来五分钟限价订单簿的成交量-价格分布，相比MSE训练将全字段传输成本降低45.8%。
该论文提出 Control-TS，通过残差控制将预测分位数作为分布控制信号引入预训练扩散预测模型，并提出 CLPS 指标评估生成未来轨迹的下游预测效用，在八个基准上一致提升 CRPS 表现。
Researchers from KAIST, Kookmin University, and NYU propose a proper scoring rule-based diffusion framework for probabilistic weather forecasting that adds auxiliary conditional denoising tasks to CRPS-trained models, enabling improved forecast accuracy and calibration at longer horizons while keeping single-forward-pass ensemble generation.
The paper introduces fable.intermittent, an R package for benchmarking probabilistic forecasting methods for intermittent time series, and presents TWEES, a new exponential smoothing model using Tweedie distribution.
This paper proposes a generative approach using neural temporal point processes for probabilistic forecasting in business process monitoring, demonstrating superior calibration and inference efficiency over existing discriminative models.
In this podcast episode, Peter Battaglia and Fry explore WeatherNext 3, an AI model for weather forecasting, discussing its applications in hurricane tracking and renewable energy grid optimization, along with probabilistic forecasting and future outlooks.
DynG-Diff proposes a state-aware dynamic guidance diffusion framework for probabilistic multivariate time series forecasting, improving robustness by adaptively handling variable heterogeneity.
The paper compares foundation models with market-specific benchmarks for electricity price forecasting, finding that only TabPFN consistently outperforms statistically, but economic value varies by strategy and risk tolerance, suggesting foundation models cannot universally replace tailored models.
SimCast-S2S is a generative latent-diffusion framework for probabilistic subseasonal precipitation forecasting that leverages transfer learning from climate simulations to outperform deep learning baselines and compete with operational systems.
This study systematically evaluates TabPFN-TS for zero-shot probabilistic heat load forecasting in district heating networks, comparing it with state-of-the-art time-series foundation models like Chronos-2 and machine-learning baselines.
TinyCast is a compact zero-shot time series foundation model with computed periodicity, enabling efficient probabilistic forecasting on edge devices like Cortex-M7.
This paper proposes DiffDiff, a diffusion framework for probabilistic time series forecasting that embeds predictability asymmetry into the diffusion trajectory, outperforming six diffusion baselines on seven benchmarks across four prediction horizons.
The paper proposes a verifiable label-free reward for training calibrated probabilistic forecasters using reinforcement learning, avoiding the calibration degradation that occurs when rewarding single outcomes. Applied to NFL win probability, a 7B model trained with this reward achieves calibration comparable to the betting market.
This paper introduces Explanation Quality Markers (EQMs), a set of 60 reasoning patterns scored by LLMs to measure the quality of natural-language explanations in forecasting tournaments. Analyzing over 55,000 forecast-rationale pairs, EQMs predict accuracy at both forecast and forecaster levels, outperforming previous methods.
Otter Weather is a computationally efficient AI model for medium-range weather forecasting that outperforms numerical weather prediction baselines and frontier AI models while requiring significantly less training compute, aiming to democratize high-performance weather prediction.
This paper introduces regime-stratified evaluation for time series foundation models, revealing that aggregate metrics hide severe failures during traffic regime transitions, and proposes bimodal mixture augmentation to improve coverage while preserving overall accuracy.
SAGA introduces a decoder-only transformer for multi-horizon probabilistic forecasting of lifetime earnings, paired with adaptive conformal prediction to provide reliable prediction intervals. Trained on a large Swedish register dataset, it achieves significant improvements over traditional parametric and baseline models.