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

Understand

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.