{
  "abstract": "Reservoir computing (RC) offers an efficient data-driven approach for forecasting extreme events (EEs), which correspond to rare and large-amplitude dynamical occurrences. We propose a dual-stage cascade framework that jointly predicts both the timing and peak intensity of upcoming EEs. A traditional RC branch integrates long-term precursor dynamics to support stable detection and long-horizon prediction, while an NGRC branch captures local nonlinear waveform geometry to improve fine-grained time-to-peak localization and complement peak-intensity estimation. The fused features then feed a ridge classifier that issues a binary alarm upon detecting precursors. Only then do two ridge regressors, trained on true-positive snapshots, estimate time-to-peak and peak intensity. This classify-then-regress design addresses severe class imbalance without data resampling. Evaluated on simulated pump-modulated VCSEL data, the hybrid model achieves a SEDI value >0.8, with MAEs around 0.2 ns and 0.2 a. u., maintaining performance up to a 20 ns warning horizon. The framework advances extreme-event forecasting from binary warnings to fully quantitative dual-objective prediction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.04229",
  "author_total": 3,
  "authors": [
    {
      "name": "Yueyang Wang"
    },
    {
      "name": "Juncheng Huang. Hanxu Zhou"
    },
    {
      "name": "Tao Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0exqcgbp9m5b1.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.04229",
  "fulltext_url": null,
  "id": "0exqcgbp9m5b1",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.04229",
  "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",
      "physics.optics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.04229",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0exqcgbp9m5b1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:01Z",
      "native_id": "2610.04229",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.04229"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.optics",
      "subfield": "optics-photonics"
    }
  ],
  "title": "Joint Forecasting of Extreme Events through Dual-Stage Cascade Reservoir Computing",
  "title_text": "Joint Forecasting of Extreme Events through Dual-Stage Cascade Reservoir Computing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:01Z",
  "url": "https://research.draconitic.com/paper/0exqcgbp9m5b1",
  "venue": "arXiv",
  "versions": []
}