Continuous-time models such as Neural ODEs and Neural Flows have shown promising results in analyzing irregularly sampled time series frequently encountered in electronic health records. Based on these models, time series are typically processed with a hybrid of an initial value problem (IVP) solver and a recurrent neural network within the variational autoencoder architecture. Sequentially solving IVPs makes such models computationally less efficient. In this paper, we propose to model time series purely with continuous processes whose state evolution can be approximated directly by IVPs. This eliminates the need for recurrent computation and enables multiple states to evolve in parallel. We further fuse the encoder and decoder with one IVP solver based on its invertibility, which leads to fewer parameters and faster convergence. Experiments on three real-world datasets show that the proposed approach achieves comparable extrapolation and classification performance while gaining more than one order of magnitude speedup over other continuous-time counterparts.

翻译：诸如神经常微分方程和神经流等连续时间模型在处理电子健康记录中常见的不规则采样时间序列方面展现出良好前景。基于这些模型，时间序列通常通过变分自编码器架构中的初值问题求解器与循环神经网络的混合方式进行处理。顺序求解初值问题使得此类模型计算效率较低。本文提出完全采用连续过程对时间序列进行建模，其状态演化可直接通过初值问题逼近。这消除了循环计算的需求，并使多个状态能够并行演化。我们进一步基于初值问题求解器的可逆性将编码器与解码器融合为一个求解器，从而减少参数数量并加快收敛速度。在三个真实世界数据集上的实验表明，所提出的方法在实现可比较的外推和分类性能的同时，相较于其他连续时间方法获得了一个数量级以上的加速比。