HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting

• URL: http://arxiv.org/abs/2201.09101v1
• Date: Sat, 22 Jan 2022 17:30:46 GMT
• Title: HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
• Authors: Minbo Ma, Peng Xie, Fei Teng, Tianrui Li, Bin Wang, Shenggong Ji, Junbo Zhang
• Abstract summary: We propose a novel Graph Hierarchical Spatio-Temporal Neural Network (HiSTGNN) to model cross-regional-temporal correlations among meteorological variables in multiple stations. Experimental results on three real-world meteorological datasets demonstrate the superior performance of HiSTGNN beyond 7 baselines. It reduces the errors by 4.2% to 11.6% especially compared to state-of-the-art weather forecasting method.
• Score: 13.317147032467306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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