Recurrent Neural Networks for Multivariate Time Series with Missing Values
Name
s41598-018-24271-9.pdf
Description
Published version
Size
1.87 MB
Format
Adobe PDF
Checksum (MD5)
18f49886fe527068077d36c3c5fe6a3f
Author(s)
Che, Zhengping
Purushotham, Sanjay
Cho, Kyunghyun
Sontag, David
Liu, Yan
Date Issued
2018
Journal
Scientific Reports
Publisher
Springer Nature
Version
Final published version
Abstract
© 2018 The Author(s). Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41598-018-24271-9
