{
  "id": 10839702,
  "title": "JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements",
  "url": "https://urgent.news/2026/09/29/judgecast-time-series-forecasting-with-experience-informed-covariate",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-29T08:02:33.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.36966v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Covariate effects vary across contexts and shift over time, requiring forecasters to assess how to use them for each forecasting context. As forecasting proceeds, observations for earlier forecasts become available, providing feedback on past covariate use for subsequent forecasts. However, when multiple covariates act together, the forecast error reveals the numerical discrepancy from the…",
  "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."
}