{
  "abstract": "Change points that arrive in quick succession, or near the boundaries of the sample, are prone to type-II error: the few observations trapped between successive breaks carry too little information for global procedures to analyze. This article develops a test statistic, $I_1$, for which we derive the limiting null distribution under Gaussian noise, extend the theory to weakly dependent sequences, and prove consistency under a mean-shift alternative. Around $I_1$, we construct DCI, a two-phase algorithm that couples a global scan with multiple local tests via an adaptive-width sliding window. In extensive simulations, $I_1$ and DCI remain Pareto-optimal over criteria spaces spanning true and false positive rates, localization error, and segmentation covering. An application to the US-Iran 2026 conflict quantifies the exposure of energy markets to geopolitical shocks using two new metrics, event sensitivity and event attribution, leading to time series tokenization.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.23476",
  "author_total": 3,
  "authors": [
    {
      "name": "Nachiketas Waychal"
    },
    {
      "name": "Arnab Kumar Laha"
    },
    {
      "name": "Ankur Sinha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4sz4h8k1j3307.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.23476",
  "fulltext_url": null,
  "id": "4sz4h8k1j3307",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.23476",
  "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.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1804|Advanced Statistical Process Monitoring"
    ]
  ],
  "openalex": "W7222938315",
  "pdf_url": "https://arxiv.org/pdf/2610.23476",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4sz4h8k1j3307.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:43Z",
      "native_id": "2610.23476",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.23476"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Closely spaced change-point detection with an application to geopolitical shocks in oil and gas markets",
  "title_text": "Closely spaced change-point detection with an application to geopolitical shocks in oil and gas markets",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:38Z",
  "url": "https://research.draconitic.com/paper/4sz4h8k1j3307",
  "venue": "arXiv",
  "versions": []
}