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基于自注意力机制LSTM的COVID-19感染预测

VIRUS PROPAGATION PREDICTION BASED ON LSTM-SELF-ATTENTION
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摘要 COVID-19因各国气候、政府政策和疫苗接种人数等因素的不同而呈现不同的发展趋势,这导致COVID-19数据不稳定,传统的机理模型无法根据历史时序数据做出准确预测。因此,提出一种在深度学习LSTM网络框架下引入Self-Attention机制的改进模型。通过仿真实验,对中国、英国和意大利的COVID-19现存病例数据进行预测,并与带有非线性传染率的SIS模型、LSTM模型和ConvLSTM模型的预测结果对比,实验证明,相比于其他三种模型,LSTM-Self-Attention模型的预测精度更高。 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.
作者 吴昊 曹宇 魏海平 田壮 Wu Hao;Cao Yu;Wei Haiping;Tian Zhuang(College of Computer and Communication Engineering,Liaoning Petrochemical University,Fushun 113000,Liaoning,China)
出处 《计算机应用与软件》 北大核心 2024年第9期106-113,共8页 Computer Applications and Software
基金 辽宁省教育科学“十三五”规划立项重点课题(JG18DA013) 辽宁省重点研究开发项目(2020JH2/10300040) 辽宁省教育厅资助课题(L2020031)。
关键词 COVID-19 SIS 自注意力机制 长短期记忆网络 ConvLSTM COVID-19 SIS Self-Attention LSTM ConvLSTM
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