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Recurrent Neural Networks for Multivariate Time Series with Missing Values

Scientific reports, 2018-04, Vol.8 (1), p.6085-12, Article 6085 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-24271-9 ;PMID: 29666385

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  • Title:
    Recurrent Neural Networks for Multivariate Time Series with Missing Values
  • Author: Che, Zhengping ; Purushotham, Sanjay ; Cho, Kyunghyun ; Sontag, David ; Liu, Yan
  • Subjects: Neural networks ; Time series
  • Is Part Of: Scientific reports, 2018-04, Vol.8 (1), p.6085-12, Article 6085
  • Description: 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.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-018-24271-9
    PMID: 29666385
  • Source: AUTh Library subscriptions: ProQuest Central
    PubMed Central
    DOAJ Directory of Open Access Journals
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