Abstract:In order to effectively utilize the time information of the ionospheric total electron content (TEC) series, we propose a prediction model combining the empirical orthogonal function decomposition and the neural network of long and short memory. We use the TEC grid data of Yunnan region provided by IGS to model and forecast the ionosphere at different locations and different periods. The experimental results of the prediction model in this paper show that the optimal root mean square error of TEC values for 5 days in the same period of time is 1.83 TECu, which is reduced by 16% compared with the single model, and the optimal average relative accuracy is 91.56%, which is increased by 7% compared with the single model. The optimal root mean square error of TEC values at the same location for 5 days is 1.86 TECu, which is 25% less than that of the single model, and the optimal average relative accuracy is 90.74%, which is 7% more than that of the single model.