{
  "abstract": "Trajectory extrapolation from a partial target trajectory is underdetermined, but completed related trajectories provide information about the unresolved continuation. We propose target-constrained pooling (TCP), which separates the finite-horizon endpoint from continuation shape and estimates that endpoint from two complementary source signals. Uniform pooling captures final response levels, while matched-increment pooling captures the remaining change after aligning source and target states. TCP selects representative sources, projects both endpoint estimates onto a target-consistent region, converts them into corrections of the free forecast, and controls their influence through strength calibration and degradation-aware weighting. Across 25,245 targets from five domains and both forecasting directions, TCP reduces mean RMSE by 35.8% to 42.1% for all ten target-only forecasters and improves all 100 forecaster, dataset, and direction comparisons. The two endpoint signals consistently outperform either alone, while representative selection remains within 0.64% to 1.02% of the full donor pool using 12.7x fewer donor inputs and 138.8x fewer matching distance computations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13034",
  "author_total": 3,
  "authors": [
    {
      "name": "Ning Wang"
    },
    {
      "name": "Claire Cardie"
    },
    {
      "name": "Sainyam Galhotra"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8834e21bcbnfp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13034",
  "fulltext_url": null,
  "id": "8834e21bcbnfp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13034",
  "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.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Target Tracking and Data Fusion in Sensor Networks"
    ]
  ],
  "openalex": "W7221576077",
  "pdf_url": "https://arxiv.org/pdf/2610.13034",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8834e21bcbnfp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:42Z",
      "native_id": "2610.13034",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13034"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Target-Constrained Pooling for Trajectory Extrapolation",
  "title_text": "Target-Constrained Pooling for Trajectory Extrapolation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:42Z",
  "url": "https://research.draconitic.com/paper/8834e21bcbnfp",
  "venue": "arXiv",
  "versions": []
}