{
  "abstract": "Time series forecasting is fundamental to numerous fields, yet long-range dependencies, multivariate coupling, and nonlinear trend capture remain persistent challenges, especially in fine-grained 24 h surface temperature prediction. To address these issues, this paper presents a novel Cross-Channel Temporal Correlation Transformer (CCT). Its core innovations are threefold: (i) A multi-branch adaptive fusion architecture that, for the first time in time series forecasting, dynamically integrates temporally enhanced, multi-scale trend, and original embedding features through a learnable gated attention mechanism, avoiding the information loss inherent in single-branch designs. (ii) A cross-temporal multi-head attention block augmented with relative positional bias and a parallel local convolutional enhancement branch, which jointly capture global patch relationships and fine-grained local dynamics. (iii) A trend-aware attention module that employs a learnable group of multi-scale convolutions and cross-variable attention, enabling adaptive trend decomposition and interaction beyond fixed-scale moving averages. These components enable the decoupled modeling and adaptive fusion of daily-scale weather evolution and hourly-scale details. Evaluated on a meteorological dataset covering six representative Chinese cities across coastal and inland climates, CCT outperforms 12 mainstream models, achieving an average MSE reduction of 6.2% and MAE reduction of 7.5%. In extreme heat conditions such as Chongqing, the MSE reduction exceeds 9%.",
  "abstract_shown": true,
  "affiliations": [
    "School of Mathematics, Hefei University of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qianji Ding"
    },
    {
      "affs": [
        0
      ],
      "name": "Xing Huo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ch97pxbbp1jca.bib",
  "cited_by": 0,
  "doi": "10.1007/s44274-026-01044-0",
  "fulltext_url": "https://doi.org/10.1007/s44274-026-01044-0",
  "id": "ch97pxbbp1jca",
  "issns": [
    "2731-9431"
  ],
  "landing_url": "https://doi.org/10.1007/s44274-026-01044-0",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "openalex",
      "1711|Time Series Analysis and Forecasting"
    ]
  ],
  "openalex": "W7223178053",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s44274-026-01044-0.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/ch97pxbbp1jca.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:50Z",
      "native_id": "d5779325488e42799a61697f6bd4af2a",
      "source": "doaj",
      "url": "https://doi.org/10.1007/s44274-026-01044-0"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "lcc:GE1-350",
      "subfield": "environmental-science"
    }
  ],
  "title": "A cross-channel temporal correlation transformer for diurnal near-surface air temperature prediction from multivariate observations",
  "title_text": "A cross-channel temporal correlation transformer for diurnal near-surface air temperature prediction from multivariate observations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:50Z",
  "url": "https://research.draconitic.com/paper/ch97pxbbp1jca",
  "venue": "Discover Environment",
  "versions": []
}