{
  "abstract": "Conformal prediction provides distribution-free coverage under exchangeability, but global calibration can conceal heterogeneous forecast errors. We introduce attribution-state conformal forecasting (ASCF), which learns calibration strata from residual-independent local explanation geometry. Exact TreeSHAP contributions are normalized, embedded in a square-root/Hellinger space, and clustered before residuals or test outcomes are observed. ASCF was evaluated on 1,381,005 source-level greenhouse-gas observations using separate fitting, state-learning, calibration, and test roles. At 90% nominal coverage, ASCF achieved coverage/interval-score pairs of 0.899/1.693 in the terminal temporal evaluation, 0.927/1.622 for unseen sources, and 0.929/2.294 across five leave-one-country-out folds in which ASCF was instantiable. Relative to global CP, interval-score reductions were 15.9%, 13.9%, and 18.6%. ASCF also produced lower paired interval scores than prediction-conditioned, raw-K matched, and country × gas Mondrian CP in three evaluations, with bootstrap support, reinforcing ASCF’s advantage among the residual-independent conditioning strategies evaluated. Persistence and adapted residual-aware grouping attained lower interval scores. Persistence showed stronger point-forecast accuracy, while an ASCF-plus-global-CP fallback policy extended geographic prediction to all eight countries. In a synthetic proof-of-mechanism, attribution states recovered oracle mechanisms better than matched raw-feature clustering (ARI 0.659 versus 0.040). SHAP-representation sensitivity affected state quality. Under exchangeability, a frozen state map inherits group-conditional split-conformal validity; deliberate shift experiments are interpreted empirically.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Information Technology, NED University of Engineering and Technology, Karachi 75270, Pakistan",
    "Department of Electrical Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shiza Riaz Memon",
      "orcid": "0009-0006-5482-5747"
    },
    {
      "affs": [
        1
      ],
      "name": "Saad Ahmed Qazi",
      "orcid": "0000-0002-1522-0677"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b9r8rv7j9q79b.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213990",
  "fulltext_url": null,
  "id": "b9r8rv7j9q79b",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3990",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/b9r8rv7j9q79b.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:23:28Z",
      "native_id": "10.3390/math14213990",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3990"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Attribution-State Conformal Forecasting: Residual-Independent Explanation States for Uncertainty Calibration Under Distribution Shift",
  "title_text": "Attribution-State Conformal Forecasting: Residual-Independent Explanation States for Uncertainty Calibration Under Distribution Shift",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T00:20:49Z",
  "url": "https://research.draconitic.com/paper/b9r8rv7j9q79b",
  "venue": "Mathematics",
  "versions": []
}