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:一个关键问题是,自然和人工过程产生的数据往往包含不定期抽样和/或缺少观测的时间序列。在这项工作中,我们提议通过离散时间观测对时间序列进行连续时间建模的神经连续分解国家空间模型(NCDSSM)。全国空间和空间安全委员会使用辅助变量来分解对动态的识别,从而只要求辅助变量进行分解引。我们从连续分辨过滤理论中汲取技术,我们展示如何为动态状态进行准确的贝叶斯推论。我们建议对潜在动态进行三个灵活的参数化,并提出一项有效的培训目标,在推断过程中将动态状态边缘化。关于多个领域基准数据集的实证结果显示,国家空间和空间和空间安全委员会对现有模型的预测性能有所改善。