Recurrent Neural Networks for Multivariate Time Series with Missing Values View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, Yan Liu

ABSTRACT

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. More... »

PAGES

6085

References to SciGraph publications

  • 2010-10. Wavelet variance analysis for gappy time series in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • 2016-05-24. MIMIC-III, a freely accessible critical care database in SCIENTIFIC DATA
  • 2010-03. Pattern classification with missing data: a review in NEURAL COMPUTING AND APPLICATIONS
  • 2016. DeepCare: A Deep Dynamic Memory Model for¬†Predictive Medicine in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-018-24271-9

    DOI

    http://dx.doi.org/10.1038/s41598-018-24271-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103353461

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29666385


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