Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models

Due to the high uncertainties in temperature changes, traditional regression analysis and time series prediction methods fail to provide accurate temperature forecasts to reduce the impact of extreme weather on human society. Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. The model uses the T-GCN model to capture spatiotemporal features while introducing Luong attention to weight the inputs at different time steps to improve the prediction accuracy and further reduce the prediction error by fusing the outputs of the T-GCN-Luong attention and GRU models through the variable weight combination method. The results revealed that (1) the inclusion of spatial information significantly improved the effectiveness of the temperature predictions. (2) The Luong attention mechanism weights different time steps and improves the prediction accuracy of the T-GCN model. (3) The TGLAG combination model constructed via the variable weight method exhibited good predictive performance at 15 sites. Compared with that of the simple GRU model, the accuracy of the proposed model is improved by approximately 31.949% in terms of the root mean square error (RMSE) and 26.913% in terms of the mean absolute error (MAE). Compared with the second-best model, T-GCN-Luong attention, the TGLAG model yields a 5.946% lower RMSE and 9.535% lower MAE, which indicates that TGLAG has good application prospects in the field of temperature prediction.