Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data

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: this https URL.

26 pages, 10 figures, 25 tables. Extended version of the paper accepted at CIKM 2026. The conference proceedings version contains Appendix A only

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2607.15606 [cs.LG]

(or arXiv:2607.15606v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.15606 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3799682.3841108 Focus to learn more DOI(s) linking to related resources

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