Modeling real-world multidimensional time series can be particularly
challenging when these are sporadically observed (i.e., sampling is irregular
both in time and across dimensions)-such as in the case of clinical patient
data. To address these challenges, we propose (1) a continuous-time version of
the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential
Equations (Chen et al., 2018), and (2) a Bayesian update network that processes
the sporadic observations. We bring these two ideas together in our
GRU-ODE-Bayes method. We then demonstrate that the proposed method encodes a
continuity prior for the latent process and that it can exactly represent the
Fokker-Planck dynamics of complex processes driven by a multidimensional
stochastic differential equation. Additionally, empirical evaluation shows that
our method outperforms the state of the art on both synthetic data and
real-world data with applications in healthcare and climate forecast. What is
more, the continuity prior is shown to be well suited for low number of samples
settings.

提出了一种 GRU-ODE-Bayes 方法来建模真实世界的多维时间序列，该方法包括对神经普通微分方程的连续时间版本和处理不规则数据采样的贝叶斯更新网络，并证明了该方法在医疗保健和气候预测领域的应用中优于现有技术。