Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data

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Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data
Title:
Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data
Journal Title:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywords:
Publication Date:
27 April 2022
Citation:
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
Abstract:
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 [1] and PhysioNet Challenge 2012 [2]. Performance study results show the superiority of the proposed learning algorithm.
License type:
Publisher Copyright
Funding Info:
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
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISSN:
2379-190X
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