{
  "id": 17925882,
  "title": "Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models",
  "url": "https://urgent.news/2026/11/02/evaluating-uncertainty-calibration-in-probabilistic-time-series",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-02T18:34:50.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2611.03592v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}