Abstract: To address the challenges of sparsity and missingness in short-term high-dimensional data, this study proposes a multivariate time series imputation algorithm based on the generalized (q, p) embedding space. The proposed algorithm simultaneously extracts the multivariate joint state information from the q preceding and p succeeding time points around the target imputation node, and establishes bidirectional constraints on the intrinsic relationships among variables, thereby achieving missing value imputation and data augmentation for high-dimensional complex data. Experiments on Traditional Chinese Medicine clinical data for hypertension yield two principal findings. First, regarding predictive inference, the time series data augmented by the proposed algorithm yield a 13.0% improvement in the classification accuracy of the "syndrome of intermin-gled phlegm and blood stasis" based on Long Short-Term Memory. Second, regarding missing value imputation, the binary classification accuracies for the diagnosis of phlegm syndrome and blood stasis syndrome reach 84.2% and 77.2%, respectively.

Key words: Takens' theorem, random distributed embedding, multivariate time series imputation, long short-term memory, hypertension, syndrome of intermin-gled phlegm and blood stasis