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

Nexus sine qua non: Essentially connected neural networks for  spatial-temporal forecasting of multivariate time series

As a core technology of Intelligent Transportation System, traffic flow
prediction has a wide range of applications. The fundamental challenge in
traffic flow prediction is to effectively model the complex spatial-temporal
dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN)
models have emerged as one of the most promising methods to solve this problem.
However, GNN-based models have three major limitations for traffic prediction:
i) Most methods model spatial dependencies in a static manner, which limits the
ability to learn dynamic urban traffic patterns; ii) Most methods only consider
short-range spatial information and are unable to capture long-range spatial
dependencies; iii) These methods ignore the fact that the propagation of
traffic conditions between locations has a time delay in traffic systems. To
this end, we propose a novel Propagation Delay-aware dynamic long-range
transFormer, namely PDFormer, for accurate traffic flow prediction.
Specifically, we design a spatial self-attention module to capture the dynamic
spatial dependencies. Then, two graph masking matrices are introduced to
highlight spatial dependencies from short- and long-range views. Moreover, a
traffic delay-aware feature transformation module is proposed to empower
PDFormer with the capability of explicitly modeling the time delay of spatial
information propagation. Extensive experimental results on six real-world
public traffic datasets show that our method can not only achieve
state-of-the-art performance but also exhibit competitive computational
efficiency. Moreover, we visualize the learned spatial-temporal attention map
to make our model highly interpretable.

该研究提出了一种基于动态远程传输的新型传播延迟感知长输入 Transformer，能够准确预测交通流量，并在真实交通数据集上取得了最先进的性能和计算效率。