论文标题
多元时间序列中信息缺失的神经ODES
Neural ODEs for Informative Missingness in Multivariate Time Series
论文作者
论文摘要
在连续时间序列的数字处理中，不可避免的是信息性的缺失，在不同时间点，一个或多个观察值的价值丢失了。这种缺失的观察是使用深度学习的时间序列处理的主要局限性之一。实际应用，例如，传感器数据，医疗保健，天气，生成了实际上是连续及时连续的数据，而信息性的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。该特征使时间序列预测更具挑战性，并且缺失的输入观察结果对最终产出的准确性的影响可能很重要。最近一种名为GRU-D的新颖的深度学习模型是解决时间序列数据中内容丰富的缺失的一种早期尝试。另一方面，一个称为神经odes（普通微分方程）的新神经网络家族是自然而有效的，用于处理时间序列数据，这是及时连续的。在本文中，提出了一个深度学习模型，该模型利用了GRU-D的有效插补以及神经ODE的时间连续性。在Physionet数据集上执行的时间序列分类任务演示了此体系结构的性能。
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.
