{
  "abstract": "We introduce fastC-GC and fastC-GC$^*$, computationally efficient methods for time-series graph estimation using the two-test rule motivated by Reichenbach's common cause principle (RCCP) and the conditioning sets of causalised Granger causality (c-GC and c-GC$^*$). The methods use standardized data, ridge-residual correlation, and complete circular shift enumeration evaluated by Fourier transforms. Shared precision calculations and Schur downdates avoid repeated regression fits. On a single-threaded benchmark, fastC-GC was 68-277 times faster than the compared c-GC implementation, and fastC-GC$^*$ was 192-886 times faster than c-GC$^*$. Across four suites with reference graphs, aggregate mean area under the precision-recall curve (AUPRC) differed from the corresponding baseline by at most 0.019, but F1 was lower at the selected thresholds in every suite. The computational gains therefore accompany a change in estimator and selected graphs. Among eight methods, Bagged PCMCI+ had the highest mean AUPRC on sparse VAR, an autocorrelated structural process, and DREAM3; CUTS+ led on Lorenz-96. Wind-tunnel and river-discharge applications illustrate differences between inferred graphs without establishing causal accuracy. We prove the computational identities and a restricted population adjacency result for fastC-GC$^*$. Causal interpretation requires the stated graphical assumptions; validity of the implemented tests additionally requires a suitable shift-invariant null law. Empirical null rejection rates informed the conditional threshold $β= 0.005$, without establishing general test validity or graph-wide error control.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.37883",
  "author_total": 1,
  "authors": [
    {
      "name": "S. A. Adedayo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7b1p0bb4w63t3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.37883",
  "fulltext_url": null,
  "id": "7b1p0bb4w63t3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.37883",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.SI"
    ],
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.CO"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.37883",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7b1p0bb4w63t3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:41Z",
      "native_id": "2610.37883",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.37883"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.CO",
      "subfield": "statistics"
    }
  ],
  "title": "fastC-GC and fastC-GC*: Efficient RCCP-Guided Graph Estimation for Time Series",
  "title_text": "fastC-GC and fastC-GC*: Efficient RCCP-Guided Graph Estimation for Time Series",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/7b1p0bb4w63t3",
  "venue": "arXiv",
  "versions": []
}