{
  "abstract": "Non-stationarity induces test-time distribution shifts in time series forecasting (TSF), resulting in forecast residuals between predictions and the corresponding ground truth. Recent works on test-time adaptation for TSF address this by introducing lightweight calibration modules around frozen forecasters, updated using observations that become incrementally available at test time. Existing approaches typically employ channel-wise adapters, implicitly assuming that forecast residuals are inherently channel-specific. However, we observe that forecast residuals often exhibit substantial shared structure across channels, suggesting that fully independent channel-wise parameterization may be redundant. Motivated by this observation, we propose the Dual-Branch Calibration Module (DBCM), which shares adaptation capacity across channels through a shared adapter while retaining limited channel-specific flexibility through a low-rank channel-wise adapter, further enhanced by channel mixing and nonlinear gated modulation. Extensive experiments show that DBCM consistently outperforms prior methods across diverse datasets, horizons, and backbones.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.32071",
  "author_total": 4,
  "authors": [
    {
      "name": "Jaebyeong Jeon"
    },
    {
      "name": "Kunwoo Kang"
    },
    {
      "name": "Sang Yup Lee"
    },
    {
      "name": "Kibok Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1tnszycc44ff0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.32071",
  "fulltext_url": null,
  "id": "1tnszycc44ff0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.32071",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1711|Time Series Analysis and Forecasting"
    ]
  ],
  "openalex": "W7223676715",
  "pdf_url": "https://arxiv.org/pdf/2610.32071",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1tnszycc44ff0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:25Z",
      "native_id": "2610.32071",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.32071"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "DBCM: Dual-Branch Calibration Module for Test-Time Adaptation in Time Series Forecasting",
  "title_text": "DBCM: Dual-Branch Calibration Module for Test-Time Adaptation in Time Series Forecasting",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:25Z",
  "url": "https://research.draconitic.com/paper/1tnszycc44ff0",
  "venue": "arXiv",
  "versions": []
}