We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there is a latent graph between the time series (nodes) that governs the evolution of the multivariate time series. By parameterizing a graph in a differentiable way, the models aim to improve forecasting quality. We compare four recent models of this class on the forecasting task. Further, we perform ablations to study their behavior under changing conditions, e.g., when disabling the graph-learning modules and providing the ground-truth relations instead. Based on our findings, we propose novel ways of combining the existing architectures.

翻译：我们研究最近一组模型,这些模型使用图形神经网络(GNNs)来改进多变时间序列的预测。这些模型的核心假设是,在指导多变时间序列演变的时间序列(节点)之间有一个潜在图。通过以不同方式对一个图表进行参数化,模型的目的是提高预测质量。我们比较了该类最近四个模型的预测任务。此外,我们还进行了推算,以研究它们在不断变化的条件下的行为,例如,当破坏图形学习模块和提供地面和地面关系时。根据我们的调查结果,我们提出了将现有结构结合起来的新方法。