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

Excerpt

arXiv:2608.12592v2 Announce Type: replace Abstract: 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. Existing generators, however, are built around a single conditioning modality and degrade when forced to handle the heterogeneous, i