  The problem of missing values in multivariable time series is a key challenge
in many applications such as clinical data mining. Although many imputation
methods show their effectiveness in many applications, few of them are designed
to accommodate clinical multivariable time series. In this work, we propose a
multiple imputation model that capture both cross-sectional information and
temporal correlations. We integrate Gaussian processes with mixture models and
introduce individualized mixing weights to handle the variance of predictive
confidence of Gaussian process models. The proposed model is compared with
several state-of-the-art imputation algorithms on both real-world and synthetic
datasets. Experiments show that our best model can provide more accurate
imputation than the benchmarks on all of our datasets.
