研究评估时间序列基础模型的不确定性校准问题
Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models
一篇评估时间序列基础模型不确定性的论文，告诉你现有模型校准差在哪、事后重校准能救哪些、救不了哪些。
arXiv 论文 2611.03592 研究概率时间序列基础模型（TSFM）的不确定性校准。作者提出预测不确定性表示的分类法，并开发了区分 pooled、horizon-wise 和 conditional 校准的多步预测细粒度评估框架。对多个主流 TSFM 在多数据集上的实证评估显示，现有模型存在系统性误校准模式，且常被标准指标掩盖。论文还分析了常见事后重校准方法，指出它们能改善哪些校准形式、哪些会持续存在。
Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models
Time series foundation models (TSFMs) are increasingly used for probabilistic forecasting across diverse domains, yet the reliability of their uncertainty estimates remains poorly understood. In this work, we study uncertainty calibration in probabilistic TSFMs from a broader methodological and empirical perspective. We introduce a taxonomy of how TSFMs represent predictive uncertainty and develop a fine-grained evaluation framework for calibration in multi-horizon forecasting that distinguishes pooled, horizon-wise, and stronger conditional notions of calibration. Using this framework, we conduct a comprehensive empirical evaluation of popular TSFMs across diverse datasets and show that current models exhibit distinct and systematic patterns of miscalibration that standard metrics often obscure. We further analyze common post-hoc recalibration methods and characterize which forms of calibration they can improve and which tend to persist. Our results provide practical guidance for evaluating and improving uncertainty calibration in TSFMs and highlight important limitations of current practice.