Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance

翻译：时间序列预测是科学和工程多个领域中核心的现实世界应用问题。大规模时间序列数据集包含复杂模式与长期依赖关系，其丰富性催生了多种神经网络架构的发展。基于图神经网络的方法通过联合学习多变量时间序列原始值相关性的图结构并完成预测，近期取得了显著成功。然而，这类解决方案通常训练成本高且难以扩展。本文提出TimeGNN方法，该方法能够学习动态时序图表示，捕捉序列间模式随时间的演变以及多序列间的相关性。TimeGNN的推理速度比其他最先进的基于图的方法快4至80倍，同时保持相当的预测性能。