Interacting systems are prevalent in nature. It is challenging to accurately predict the dynamics of the system if its constituent components are analyzed independently. We develop a graph-based model that unveils the systemic interactions of time series observed at irregular time points, by using a directed acyclic graph to model the conditional dependencies (a form of causal notation) of the system components and learning this graph in tandem with a continuous-time model that parameterizes the solution curves of ordinary differential equations (ODEs). Our technique, a graph neural flow, leads to substantial enhancements over non-graph-based methods, as well as graph-based methods without the modeling of conditional dependencies. We validate our approach on several tasks, including time series classification and forecasting, to demonstrate its efficacy.

翻译：自然界中普遍存在相互作用系统。若将其构成组件独立分析，则难以准确预测系统动态。本文提出一种基于图的模型，通过使用有向无环图建模系统组件的条件依赖关系（一种因果表示形式），并结合参数化常微分方程解曲线的连续时间模型进行联合学习，从而揭示不规则时间点观测到的时间序列之间的系统性交互作用。我们的技术——图神经流，相较于非图方法以及未建模条件依赖关系的图方法，均取得了显著性能提升。我们在时间序列分类与预测等多个任务上验证了本方法的有效性。