Graph-Based Differential Equation Network for Medium-Range Temperature Forecasting

Medium-range temperature forecasts are of critical importance for a diverse range of economic activities, e.g., agricultural production, transportation, and industrial operations. The most significant challenge in accurately predicting temperature lies in effectively characterizing the spatiotemporal evolution of temperature characteristics. In this paper, we design a spatial feature module and a gating temporal module based on the location-oriented directed adjacency matrix. Dynamic evolution of both spatial and temporal features is characterized by a graph-based differential equation module. The spatial and temporal feature sequences are integrated by an output fusion module to achieve medium-range temperature prediction. The proposed graph-based differential equation network has been validated on the dataset of South China, which shows superior performance in medium-range temperature prediction.