BiCMTS: bidirectional coupled multivariate learning of irregular time series with missing values

Multivariate time series (MTS) such as multiple medical measures in intensive care units (ICU) are irregularly acquired and hold missing values. Conducting learning tasks on such irregular MTS with missing values, e.g., predicting the mortality of ICU patients, poses significant challenge to existing MTS forecasting models and recurrent neural networks (RNNs), which capture the temporal dependencies within a time series. This work proposes a bidirectional coupled MTS learning (BiCMTS) method to represent both forward and backward value couplings within a time series by RNNs and between MTS by self-attention networks; the learned bidirectional intra- and inter-time series coupling representations are fused to estimate missing values. We test BiCMTS on both data imputation and mortality prediction for ICU patients, showing a great potential of leveraging the deep and hidden relations captured in RNNs by the BiCMTS-learned intra- and inter-time series value couplings in MTS.