VIRUS PROPAGATION PREDICTION BASED ON LSTM-SELF-ATTENTION
COVID-19 presents different development trends due to different climate,government policies and vaccination population in different countries,which leads to the instability of COVID-19 data.The traditional mechanism model cannot make accurate prediction based on historical time series data.Therefore,this paper proposes an improved model with self-attention mechanism in the framework of deep learning LSTM network.Through simulation experiments,the existing data of COVID-19 in China,Britain and Italy were predicted,and the prediction results were compared with those of SIS model,LSTM model and ConvLSTM model with nonlinear infection rate.Experiments show that LSTM Self-Attention model has higher prediction accuracy than the other three models.
万方数据
维普
