{
  "abstract": "To better forecast mixed-frequency time series, it is the key to choose a suitable way for frequency alignment. However, the existing methods are all limited to linear transformations, and this may overlook the possible nonlinearity, leading to a worse prediction. We alternatively consider a deep neural network for each frequency alignment, and hence a depth-separable neural network. Moreover, a parameter-sharing mechanism is adopted across the alignment at each stage, making possible a deeper network for a large set of higher-frequency predictors. This paper establishes an approximation theory for the proposed depth-separable network, and a non-asymptotic prediction error bound is also derived. Simulation studies demonstrate the finite-sample performance of the proposed method, and an empirical application to forecasting U.S. quarterly macroeconomic variables using monthly and daily indicators, highlights its superior predictive accuracy over existing mixed-frequency methods.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.14771",
  "author_total": 3,
  "authors": [
    {
      "name": "Yize Wang"
    },
    {
      "name": "Qianqian Zhu"
    },
    {
      "name": "Guodong Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/akhwxsgvw02tk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.14771",
  "fulltext_url": null,
  "id": "akhwxsgvw02tk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.14771",
  "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",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.14771",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/akhwxsgvw02tk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:48Z",
      "native_id": "2607.14771",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.14771"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks",
  "title_text": "Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:48Z",
  "url": "https://research.draconitic.com/paper/akhwxsgvw02tk",
  "venue": "arXiv",
  "versions": []
}