Recurrent Neural Networks for Multivariate Time Series with Missing Values.
Recurrent Neural Networks for Multivariate Time Series with Missing Values.
复制标题
DOI：
10.1038/s41598-018-24271-9
复制
发表时间：
2018-04-17
影响因子：
4.6
通讯作者：
Liu Y
中科院分区：
文献类型：
--
作者：
Che Z;Purushotham S;Cho K;Sontag D;Liu Y
实际应用中的多变量时间序列数据，如卫生保健、地球科学和生物学，具有各种缺失值的特征。在时间序列预测和其他相关任务中，已经注意到缺失值及其缺失模式通常与目标标签相关，即信息缺失。在利用缺失模式进行有效的估算和提高预测性能方面的工作非常有限。在本文中，我们开发了新的深度学习模型，即GRU-D，作为早期的尝试之一。GRU- d基于门控循环单元（GRU），一种最先进的循环神经网络。它将缺失模式的两种表示形式，即掩蔽和时间间隔，有效地将它们结合到一个深度模型体系结构中，这样它不仅可以捕获时间序列中的长期时间依赖性，而且可以利用缺失模式来获得更好的预测结果。在真实临床数据集（MIMIC-III, PhysioNet）和合成数据集上进行的时间序列分类任务实验表明，我们的模型达到了最先进的性能，并为更好地理解和利用时间序列分析中的缺失值提供了有用的见解。
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.
影响因子：
9.8
作者：
Johnson AE;Pollard TJ;Shen L;Lehman LW;Feng M;Ghassemi M;Moody B;Szolovits P;Celi LA;Mark RG
通讯作者：
Mark RG
影响因子：
14.9
作者：
Hinton, Geoffrey;Deng, Li;Kingsbury, Brian
通讯作者：
Kingsbury, Brian
影响因子：
3.1
作者：
Azur, Melissa J.;Stuart, Elizabeth A.;Frangakis, Constantine;Leaf, Philip J.
通讯作者：
Leaf, Philip J.