Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural and artificial processes often comprise time series that are irregularly sampled and/or contain missing observations. In this work, we propose the Neural Continuous-Discrete State Space Model (NCDSSM) for continuous-time modeling of time series through discrete-time observations. NCDSSM employs auxiliary variables to disentangle recognition from dynamics, thus requiring amortized inference only for the auxiliary variables. Leveraging techniques from continuous-discrete filtering theory, we demonstrate how to perform accurate Bayesian inference for the dynamic states. We propose three flexible parameterizations of the latent dynamics and an efficient training objective that marginalizes the dynamic states during inference. Empirical results on multiple benchmark datasets across various domains show improved imputation and forecasting performance of NCDSSM over existing models.

翻译：学习现实世界动态现象（如气候、生物）的精确预测模型仍然是一项具有挑战性的任务。其中一个关键问题是，自然和人工过程生成的数据通常包含不规则采样和/或存在缺失观测的时间序列。在这项工作中，我们提出神经网络连续-离散状态空间模型（NCDSSM），用于通过离散时间观测对时间序列进行连续时间建模。NCDSSM利用辅助变量将识别过程与动力学过程解耦，从而仅需对辅助变量进行摊销推断。借助连续-离散滤波理论的技术，我们展示了如何对动态状态执行精确的贝叶斯推断。我们提出了三种灵活的潜在动力学参数化方案，以及一种高效的训练目标，该目标在推断过程中边缘化动态状态。在多个领域基准数据集上的实验结果表明，NCDSSM在插补和预测性能上优于现有模型。