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
A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approaches build forecasters with actions as covariates, trained and evaluated on prediction error under the executed plan. Yet world models compare unexecuted plans, but the...
H. Zhang, Jia-Heng Guo, Zheng Xu et al.· 0 citations
This work proposes Multi-Scale Autoencoder with Cross-Scale Attention for TSAD (MSCAD), a simple yet powerful semi-supervised TSAD framework founded on parallel autoencoder branches corresponding to different patch sizes.
Jia-Heng Guo, H. Zhang, Yu-Chao Huang et al.· 0 citations
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