{
  "abstract": "Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have recently achieved strong performance in point forecasting by jointly modeling temporal dynamics and relational dependencies across interconnected energy nodes. However, in real-world energy systems, accurate point forecasts alone are insufficient, as operators also require reliable uncertainty estimates to support risk-aware decision-making, grid stability, and operational planning under uncertainty. Conformal prediction provides a principled and model-agnostic framework for uncertainty quantification under exchangeability assumptions, making it particularly attractive for safety-critical energy applications. However, existing conformal prediction approaches often fail to fully capture the complex spatial-temporal structure of energy systems. To address these limitations, we propose STOIC (Spatial-Temporal uncertainty quantificatiOn via In-Context learning), a conformal-inspired post-hoc residual calibration framework that integrates graph-based forecasting with the in-context learning capabilities of tabular foundation models. STOIC first generates point forecasts using an STGNN and subsequently reformulates spatial-temporal residuals into a tabular representation suitable for in-context learning. Leveraging a tabular foundation model, STOIC estimates feature-conditional residual quantiles without task-specific retraining of the calibration model, effectively capturing both sequential and relational dependencies. STOIC should be viewed as a conformal-inspired residual calibration method and does not by itself provide a finite-sample distribution-free coverage guarantee under arbitrary temporal dependence...",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.31804",
  "author_total": 3,
  "authors": [
    {
      "name": "Keivan Faghih Niresi"
    },
    {
      "name": "Alice Cicirello"
    },
    {
      "name": "Olga Fink"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/296c2dzrn2enx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.31804",
  "fulltext_url": null,
  "id": "296c2dzrn2enx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.31804",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "2208|Energy Load and Power Forecasting"
    ]
  ],
  "openalex": "W7166830166",
  "pdf_url": "https://arxiv.org/pdf/2606.31804",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/296c2dzrn2enx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:48Z",
      "native_id": "2606.31804",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.31804"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs",
  "title_text": "In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:48Z",
  "url": "https://research.draconitic.com/paper/296c2dzrn2enx",
  "venue": "arXiv",
  "versions": []
}