BAYESIAN CONTINUAL IMPUTATION AND PREDICTION FOR IRREGULARLY SAMPLED TIME SERIES DATA

Learning from irregularly sampled, streaming, multi-variate time-series data with many missing values is a very challenging task. In this paper, we propose a Bayesian Continual Imputation and Prediction for Time-series Data (B-CIPIT), for learning from a sequence of time-series tasks. First, we develop a Bayesian LSTM based continual learning algorithm, which is capable of learning continually from a sequence of multi-variate time-series tasks, without catastrophically forgetting any representations. Second, we impute missing values in these time-series sequences, in a continual learning setting. We demonstrate and evaluate the robustness of the proposed algorithm on two real-world clinical time-series data sets, namely MIMIC-III \cite{johnson2016mimic} and PhysioNet Challenge 2012 \cite{9}. Performance study results show the superiority of the proposed learning algorithm.