{
  "abstract": "Large-scale astronomical surveys provide unprecedented volumes of multivariate time-series observations obtained through multiple optical filters. We develop a structured multivariate continuous-time autoregressive moving average (MCARMA) framework for multi-band time series with irregular sampling, heteroscedastic measurement errors, and partially observed bands. The framework allows band-specific stochastic dynamics while modeling cross-band dependence through correlated Brownian driving processes, with state-space and spectral representations enabling likelihood-based inference and interpretation of the fitted stochastic dynamics. We develop a two-stage estimation procedure in which a numerically stabilized preliminary fit initializes subsequent maximum likelihood estimation. Simulations show that higher-order stochastic structure can be recovered when its characteristic features are adequately resolved, but can become weakly identifiable because of limited temporal resolution or near pole--zero cancellation. Joint multivariate estimation improves parameter recovery in 23 of 27 settings and spectral recovery in 26 of 27 settings relative to separate single-band fits. Three Sloan Digital Sky Survey Stripe 82 quasars, respectively favoring MCARMA(1,0), MCARMA(2,0), and MCARMA(2,1), illustrate how joint multiband modeling uses cross-band dependence to inform marginal dynamics and can yield different model-order and spectral inference. The methodology is implemented in the Python package mcarma.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.28419",
  "author_total": 5,
  "authors": [
    {
      "name": "Izak Schmidlkofer"
    },
    {
      "name": "Zhirui Hu"
    },
    {
      "name": "Lishan Shi"
    },
    {
      "name": "Weixiang Yu"
    },
    {
      "name": "Hyungsuk Tak"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dcp6axx9fmb36.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.28419",
  "fulltext_url": null,
  "id": "dcp6axx9fmb36",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.28419",
  "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",
      "astro-ph.IM"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "1711|Time Series Analysis and Forecasting"
    ]
  ],
  "openalex": "W7214159998",
  "pdf_url": "https://arxiv.org/pdf/2609.28419",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dcp6axx9fmb36.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2609.28419",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.28419"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "arxiv:astro-ph.IM",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Multivariate Continuous-Time Autoregressive Moving Average Processes for Astronomical Multiband Time Series",
  "title_text": "Multivariate Continuous-Time Autoregressive Moving Average Processes for Astronomical Multiband Time Series",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:06Z",
  "url": "https://research.draconitic.com/paper/dcp6axx9fmb36",
  "venue": "arXiv",
  "versions": []
}