{
  "id": 5472088,
  "title": "RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting",
  "url": "https://urgent.news/2026/09/03/ratl-learning-from-retrieved-residuals-for-robust-multivariate-time",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-03T14:44:31.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.03937v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output regression, but directly reusing retrieved target values is often not robust when samples differ in output level, numerical scale, or local dynamics. Moreover, conventional forecasting pipelines generally use residuals for model optimization 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."
}