基于ARIMA-LSTM混合模型对传染病的预测分析OA
Prediction Analysis of Infectious Diseases Based on ARIMA-LSTM Mixed Model
传染病一直是科学研究的热点,利用科学的方法控制传染病的传播对整个国家乃至全世界具有举足轻重的作用.文章选取乙类传染病中新型冠状病毒感染数据作为研究对象,搜集了北京市 2022 年 1 月至 2022 年 4 月新冠感染累计确诊病例数,构成时间序列,基于自回归移动平均模型(ARIMA)和长短期记忆神经网络(LSTM)的混合模型进行预测分析.结果表明,混合模型的预测结果与实际情况基本一致.
Infectious diseases have always been a hot topic in scientific research,and using scientific methods to control the spread of infectious diseases plays a crucial role in the entire country and even the world.This paper selects COVID-19 infected persons in class B infectious diseases as the research object,collects the cumulative number of confirmed cases of COVID-19 infection in Beijing from January 2022 to April 2022,forms a time series,and conducts prediction analysis based on a mixed model of autoregressive moving average model(ARIMA)and Long Short-Term Memory(LSTM).The results indicate that the prediction results of the mixed model are basically consistent with the actual situation.
王瑞;李瑞沂;曹沛根;冯和棠;黄猛
防灾科技学院,河北 廊坊 065201
计算机与自动化
时间序列ARIMA模型LSTM模型组合预测模型
time seriesARIMA modelLSTM modelcombinatorial prediction model
《现代信息科技》 2024 (001)
116-120 / 5
防灾科技学院2022大学生创新创业项目(202211775011)
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