{
  "abstract": "Finite-horizon prediction is challenging when observations are limited and a quantity of interest depends on several coupled nonlinear measurement channels. This paper develops a three-phase data-driven twin framework that integrates finite-horizon Hankel--Koopman energy decomposition, inverse-calibrated reconstruction using nonlinear autoregressive models with exogenous inputs, and forecast composition. Channel dynamics are identified and simulated in Hankel coefficient space, with candidate models assessed after reconstruction in physical measurement space. The resulting reduced-order model of the channel dynamics drives recursive prediction of the quantity of interest. Three theorems support the framework, establishing the energy decomposition and modal orthogonality; existence, uniqueness, and inverse stability of the auxiliary continuous inverse-calibration solution; and finite-horizon stability of the composed forecast for a fixed selected model under stated assumptions. A retrospective solar-power case study yields high correlations between predicted and reference trajectories across the considered forecast horizons, demonstrating reproduction of a constructed channels-dependent quantity of interest.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.14501",
  "author_total": 2,
  "authors": [
    {
      "name": "Diana A. Bistrian"
    },
    {
      "name": "Marcel Topor"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/75260fvmaghsk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.14501",
  "fulltext_url": null,
  "id": "75260fvmaghsk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.14501",
  "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.NA"
    ],
    [
      "arxiv",
      "math.NA"
    ],
    [
      "openalex",
      "3109|Model Reduction and Neural Networks"
    ]
  ],
  "openalex": "W7213249857",
  "pdf_url": "https://arxiv.org/pdf/2609.14501",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/75260fvmaghsk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:40Z",
      "native_id": "2609.14501",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.14501"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.NA",
      "subfield": "numerical-analysis"
    }
  ],
  "title": "Hankel-Koopman Finite-Horizon Energy Decomposition of Coupled Experimental Data: A Three-Phase Data-Driven Twin Forecasting Framework",
  "title_text": "Hankel-Koopman Finite-Horizon Energy Decomposition of Coupled Experimental Data: A Three-Phase Data-Driven Twin Forecasting Framework",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:40Z",
  "url": "https://research.draconitic.com/paper/75260fvmaghsk",
  "venue": "arXiv",
  "versions": []
}