Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time series(MTS) in interconnected sensor networks, is crucial for informed decision-making in various applications to minimize risk. While graph forecasting networks(GFNs) are ideal for forecasting MTS data that exhibit spatio-temporal dependencies, prior works rely solely on the domain-specific knowledge of time-series variables inter-relationships to model the nonlinear dynamics, neglecting inherent relational structural dependencies among the variables within the MTS data. In contrast, contemporary works infer relational structures from MTS data but neglect domain-specific knowledge. The proposed hybrid architecture addresses these limitations by combining both domain-specific knowledge and implicit knowledge of the relational structure underlying the MTS data using Knowledge-Based Compositional Generalization. The hybrid architecture shows promising results on multiple benchmark datasets, outperforming state-of-the-art forecasting methods. Additionally, the architecture models the time varying uncertainty of multi-horizon forecasts.

翻译：准确预测复杂动态系统的行为，其特征为互联传感器网络中的高维多变量时间序列(MTS)，对于各类应用中的知情决策以最小化风险至关重要。虽然图预测网络(GFNs)非常适合预测具有时空依赖性的MTS数据，但先前工作仅依赖时间序列变量间相互关系的领域特定知识来建模非线性动力学，忽略了MTS数据中变量之间固有的关系结构依赖。相比之下，当代工作从MTS数据中推断关系结构，却忽视了领域特定知识。所提出的混合架构通过使用基于知识的组合泛化，结合领域特定知识和MTS数据底层关系结构的隐式知识，解决了这些局限性。该混合架构在多个基准数据集上显示出有前景的结果，其性能优于最先进的预测方法。此外，该架构还对多水平预测的时间变化不确定性进行了建模。