{
  "id": 7446893,
  "title": "Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting",
  "url": "https://urgent.news/2026/09/14/parameter-efficient-adaptation-of-pretrained-language-models-for-time",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-14T10:30:59.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2609.15344v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework, with the goal of understanding which design choices drive effective cross-modal transfer. While language models operate on discrete textual tokens, time series consist of continuous numerical observations with temporal dependencies. To bridge this…",
  "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."
}