A spatial–temporal graph attention network approach for air temperature forecasting

• DOI: 10.1016/j.asoc.2021.107888
• Publication Date: 2021-09-09T16:03:28Z
• Authors: Xuan Yu; Suixiang Shi; Lingyu Xu
• Keywords: Attention network

Abstract Air temperature prediction is a significant task for researchers and forecasters in the field of meteorology. In this paper, we present an innovative, deep spatial–temporal learning air temperature forecasting framework based on graph attention network and gated recurrent unit. Particularly, historical observations containing multiple environmental variables at different stations are constructed as graph signals. The original stations’ conditions and the learned attention information are all included in our model, which overcomes the flaw of the conventional graph network approach. Results of experiments on a real-world dataset demonstrate that, compared to the state-of-the-art baselines, our model achieves the best performance in terms of short-, middle- and long-term air temperature predictions.