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This paper presents a taxonomy-guided evaluation protocol for assessing temporal fidelity in synthetic sequential tabular data, revealing that conventional evaluation overlooks temporal failures and that rankings differ substantially when time is considered.
Timesynth introduces a controlled benchmarking framework for health-signal digital twins, including a physiological signal generator and diagnostics to evaluate temporal fidelity, revealing that pointwise metrics fail to capture phase and frequency distortions in forecasting models.