Modeling and forecasting multivariate time series not only facilitates the decision making of practitioners, but also deepens our scientific understanding of the underlying dynamical systems. Spatial-temporal graph neural networks (STGNNs) are emerged as powerful predictors and have become the de facto models for learning spatiotemporal representations in recent years. However, existing architectures of STGNNs tend to be complicated by stacking a series of fancy layers. The designed models could be either redundant or enigmatic, which pose great challenges on their complexity and scalability. Such concerns prompt us to re-examine the designs of modern STGNNs and identify core principles that contribute to a powerful and efficient neural predictor. Here we present a compact predictive model that is fully defined by a dense encoder-decoder and a message-passing layer, powered by node identifications, without any complex sequential modules, e.g., TCNs, RNNs, and Transformers. Empirical results demonstrate how a simple and elegant model with proper inductive basis can compare favorably w.r.t. the state of the art with elaborate designs, while being much more interpretable and computationally efficient for spatial-temporal forecasting problem. We hope our findings would open new horizons for future studies to revisit the design of more concise neural forecasting architectures.

本研究主要探讨了基于空间-时间图神经网络的多元时间序列建模和预测，提出了一种紧凑型的预测模型，通过节点标识驱动，不需要复杂的顺序模块，并使用稠密编码器-解码器和消息传递层，能够有效地处理空间-时间问题。实证结果表明，这种简单而优雅的模型在适当归纳基础的情况下，与复杂的设计相比，具有更好的性能，更易解释，更高效。

必要枢纽：基于本质连接神经网络的多元时间序列时空预测