{
  "abstract": "For a given point forecaster, proportional-integral-derivative (PID) calibration configurations can attain similar overall coverage yet produce different interval widths. We introduce PID-Expert, an online aggregation method for improving the efficiency of conformal prediction intervals for time series. PID-Expert combines thresholds from a fixed library of PID calibrators, each evolving under its own coverage feedback. Aggregation weights depend on normalized interval width and miscoverage, while a shared multiplier adapts the miscoverage penalty using feedback from the reported interval. We establish local regret bounds for weighted expert losses under the realized multiplier sequence and, separately, a pathwise upper bound on time-averaged aggregate miscoverage, with control in expectation and almost surely under a stability condition. Across four simulation settings and two real-data applications, PID-Expert produces narrower mean intervals than a prespecified Conformal PID benchmark while keeping overall empirical coverage close to the nominal level. Compared with expert selection and equal averaging, aggregation generally provides a more balanced coverage--width trade-off across forecasting settings. Rolling analyses further reveal local coverage--efficiency trade-offs, particularly following abrupt distributional shifts. Overall, PID-Expert reduces reliance on a single PID configuration while improving interval efficiency.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.02777",
  "author_total": 4,
  "authors": [
    {
      "name": "Guodong Liu"
    },
    {
      "name": "Yanfei Kang"
    },
    {
      "name": "Ren Miao"
    },
    {
      "name": "Xiaoqian Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ahpyaygh4sc52.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.02777",
  "fulltext_url": null,
  "id": "ahpyaygh4sc52",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.02777",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Forecasting Techniques and Applications"
    ]
  ],
  "openalex": "W7220454048",
  "pdf_url": "https://arxiv.org/pdf/2610.02777",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ahpyaygh4sc52.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:56Z",
      "native_id": "2610.02777",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.02777"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Efficient conformal prediction intervals for time series: Online PID-Expert aggregation",
  "title_text": "Efficient conformal prediction intervals for time series: Online PID-Expert aggregation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:56Z",
  "url": "https://research.draconitic.com/paper/ahpyaygh4sc52",
  "venue": "arXiv",
  "versions": []
}