{
  "abstract": "Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and research, yet conventional tabular metrics often overlook temporal structure. Existing single-table and relational evaluation protocols largely collapse records into static distributions, leaving key temporal properties insufficiently evaluated. We introduce Seq2Synth, a unified benchmark for assessing these properties. Its taxonomy characterizes temporal and schema properties to determine applicable evaluations, covering timestamp, cross-sectional, longitudinal, and structural fidelity, alongside trajectory-aware utility and privacy. Across seven core datasets from a 13-dataset benchmark and eight generators, models with near-perfect static fidelity still violate basic temporal constraints, producing duplicate timestamps, irregular intervals, and incomplete observation grids. Moreover, static and temporal-aware rankings diverge substantially, showing that temporal fidelity must be evaluated directly rather than inferred from static or relational scores. Project page and online appendices are available at: https://seq2synth.github.io/.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.15606",
  "author_total": 8,
  "authors": [
    {
      "name": "Kiwan Kwon"
    },
    {
      "name": "Kangmin Kim"
    },
    {
      "name": "Hojin Lee"
    },
    {
      "name": "Yeseong Jung"
    },
    {
      "name": "Hyeongwoo Kong"
    },
    {
      "name": "Vamsi K. Potluru"
    },
    {
      "name": "Saerom Park"
    },
    {
      "name": "Yongjae Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1ytrm2mrdpsdb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.15606",
  "fulltext_url": null,
  "id": "1ytrm2mrdpsdb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.15606",
  "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|Privacy-Preserving Technologies in Data"
    ]
  ],
  "openalex": "W7169772333",
  "pdf_url": "https://arxiv.org/pdf/2607.15606",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1ytrm2mrdpsdb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "2607.15606",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.15606"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data",
  "title_text": "Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/1ytrm2mrdpsdb",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1145/3799682.3841108",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}