arXiv Machine Learning August 12, 2026 By Kiwan Kwon, Kangmin Kim, Hojin Lee, Yeseong Jung, Hyeongwoo Kong, Vamsi K. Potluru, Saerom Park, Yongjae Lee

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data

Read the original on arXiv Machine Learning →

arXiv:2607. 15606v2 Announce Type: replace Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and data-driven research, but evaluating their fidelity remains difficult because temporal structure is easily lost under conventional tabular metrics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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