Author

H. Zhang

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Preprint Aug 2026

Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness

Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patient. Conditional generation offers a remedy: an absent signal can be synthesized from co-recorded signals and routine clinical variables. Exis...

H. Zhang, Jia-Heng Guo, Yu-Chao Huang et al. · 0 citations

#machine learning Preprint Aug 2026

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing time series generation methods predominantly focus on synthesizing normal data, providing limited value when anomalous samples are needed. We identify two fundamental chal...

H. Zhang, Jie Peng, Song-Yuan Sui et al. · 0 citations