Neural ODEs for Informative Missingess in Multivariate Time Series

M. Habiba, Barak A. Pearlmutter
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引用次数: 6

Abstract

Informative missingness is unavoidable in the digital processing of continuous time series, where the value for one or more observations at different time points are missing. Such missing observations are one of the major limitations of time series processing using deep learning. Practical applications, e.g., sensor data, healthcare, weather, generates data that is in truth continuous in time, and informative missingness is a common phenomenon in these datasets. These datasets often consist of multiple variables, and often there are missing values for one or many of these variables. This characteristic makes time series prediction more challenging, and the impact of missing input observations on the accuracy of the final output can be significant. A recent novel deep learning model called GRU-D is one early attempt to address informative missingness in time series data. On the other hand, a new family of neural networks called Neural ODEs (Ordinary Differential Equations) are natural and efficient for processing time series data which is continuous in time. In this paper, a deep learning model is proposed that leverages the effective imputation of GRU-D, and the temporal continuity of Neural ODEs. A time series classification task performed on the PhysioNet dataset demonstrates the performance of this architecture.
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多元时间序列信息缺失的神经ode
在连续时间序列的数字处理过程中,信息缺失是不可避免的,即一个或多个观测值在不同时间点的值缺失。这种缺失的观测值是使用深度学习进行时间序列处理的主要限制之一。传感器数据、医疗保健、天气等实际应用产生的数据实际上在时间上是连续的,而信息缺失是这些数据集中的常见现象。这些数据集通常由多个变量组成,并且这些变量中的一个或多个通常存在缺失值。这一特征使得时间序列预测更具挑战性,并且缺失的输入观测值对最终输出精度的影响可能是显著的。最近一种名为GRU-D的新型深度学习模型是解决时间序列数据中信息缺失的早期尝试。另一方面,一种新的神经网络称为神经常微分方程(neural ode,常微分方程),对于处理时间连续的时间序列数据是自然而有效的。本文提出了一种利用GRU-D的有效输入和神经ode的时间连续性的深度学习模型。在PhysioNet数据集上执行的时间序列分类任务演示了该体系结构的性能。
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