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arXiv · 2607.15606

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data

Kiwan Kwon·Kangmin Kim·Hojin Lee·Yeseong Jung·Hyeongwoo Kong·Vamsi K. Potluru·Saerom Park·Yongjae Lee
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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/.

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Kiwan Kwon, Kangmin Kim, Hojin Lee, Yeseong Jung, Hyeongwoo Kong, Vamsi K. Potluru, Saerom Park, Yongjae Lee. 2026-08-31. Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data. https://doi.org/10.1145/3799682.3841108

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