Guo, Y., Jun Poh, J. W., Wong, C. S. Y., & Ramasamy, S. (2022). Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp43922.2022.9746342
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  and PhysioNet Challenge 2012 . Performance study results show the superiority of the proposed learning algorithm.
This research / project is supported by the A*STAR - IAF (Industry Alignment Fund) Pre-Positioning Fund - HBMS IAF PP Dummy Grant Call Cycle 4
Grant Reference no. : H1901a0023