{
  "id": 66889,
  "title": "TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion",
  "url": "https://urgent.news/2026/07/31/tfgformer-multivariate-time-series-forecasting-via-time-frequency",
  "topic": "business",
  "section": "Business",
  "published": "2026-07-31T14:24:26.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2607.29459v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maintenance. While recent time series foundation models exhibit strong generalization, they rely on static parametric knowledge and lack dynamic access to external historical patterns during inference. Retrieval-Augmented Generation (RAG) offers a…",
  "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."
}