Tag
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.