Weather Forecasting is an attractive challengeable task due to its influence
on human life and complexity in atmospheric motion. Supported by massive
historical observed time series data, the task is suitable for data-driven
approaches, especially deep neural networks. Recently, the Graph Neural
Networks (GNNs) based methods have achieved excellent performance for
spatio-temporal forecasting. However, the canonical GNNs-based methods only
individually model the local graph of meteorological variables per station or
the global graph of whole stations, lacking information interaction between
meteorological variables in different stations. In this paper, we propose a
novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model
cross-regional spatio-temporal correlations among meteorological variables in
multiple stations. An adaptive graph learning layer and spatial graph
convolution are employed to construct self-learning graph and study hidden
dependency among nodes of variable-level and station-level graph. For capturing
temporal pattern, the dilated inception as the backbone of gate temporal
convolution is designed to model long and various meteorological trends.
Moreover, a dynamic interaction learning is proposed to build bidirectional
information passing in hierarchical graph. Experimental results on three
real-world meteorological datasets demonstrate the superior performance of
HiSTGNN beyond 7 baselines and it reduces the errors by 4.2% to 11.6%
especially compared to state-of-the-art weather forecasting method.

本文提出了一种层次化的时空图神经网络模型 (HiSTGNN)，用于模拟多个站点中气象变量之间的跨区域时空相关性，在三个真实气象数据集上表现出明显优异性能，特别是与最先进的天气预报方法相比，其误差降低了 4.2% 到 11.6%。