辽宁石油化工大学学报

辽宁石油化工大学学报 ›› 2024, Vol. 44 ›› Issue (1): 80-88.DOI: 10.12422/j.issn.1672-6952.2024.01.012

• 信息与控制工程 • 上一篇    下一篇

基于Informer算法的病毒传播预测研究

常万杰(), 刘琳琳(), 曹宇, 曹杨, 魏海平   

  1. 辽宁石油化工大学 信息与控制工程学院,辽宁 抚顺 113001
  • 收稿日期:2023-05-11 修回日期:2023-05-28 出版日期:2024-02-25 发布日期:2024-02-07
  • 通讯作者: 刘琳琳
  • 作者简介:常万杰(1996⁃),男,硕士研究生,从事机器学习、深度学习算法开发与应用方面的研究;E⁃mail:819769597@qq.com
  • 基金资助:
    辽宁省应用基础研究计划项目(2022JH2/101300272)

Research on Virus Propagation Prediction Based on Informer Algorithm

Wanjie CHANG(), Linlin LIU(), Yu CAO, Yang CAO, Haiping WEI   

  1. School of Information and Control Engineering,Liaoning Petrochemical Univercity,Fushun Liaoning 113001,China
  • Received:2023-05-11 Revised:2023-05-28 Published:2024-02-25 Online:2024-02-07
  • Contact: Linlin LIU

摘要:

新冠肺炎病毒等疫情受多种复杂现实因素的影响,因此疫情的发展存在不确定性。为了解决基于传染病仓室模型受自身诸多理想假设条件的限制而导致疫情预测结果误差较大的问题,采用基于深度学习的时序预测模型对疫情发展进行预测,建立了一种基于Transformer模型的Informer模型,并将注意力机制和蒸馏机制应用到疫情数据的时序预测中。以门限自回归(Threshold AutoRegressive, TAR)模型和多种主流的循环神经类时序预测模型作为对比模型,通过仿真实验,对中国、美国和英国的疫情数据当前尚存感染人数进行短期预测,并以均方根误差(RMSE)和平均绝对误差(MAE)为评价指标,选择最佳模型进行了中长期的预测。结果表明,无论是RMSE还是MAE,Informer模型的指标值都是最优的,表明Informer模型对中国、美国和英国疫情的预测精度比其他对比模型高。最后,使用Informer模型对中国、美国和英国的疫情发展进行了中长期预测。

关键词: 新冠肺炎病毒疫情, 门限自回归, 长短期记忆网络, 卷积记忆网络, 门控循环单元网络, 时序卷积网络, Informer算法

Abstract:

The COVID?19 epidemic is facing the influence of a variety of complex practical factors, which makes the development of the epidemic uncertain. In order to overcome the problem of large error in epidemic forecasting results due to the limitations of many ideal assumptions based on the infectious disease compartment model, a time series forecasting model based on deep learning is adopted to predict the epidemic development, and an informer model based on transformer model is established. Attention mechanism and distillation mechanism are applied to the time series forecasting of epidemic data. The threshold autoregressive (TAR) model and a variety of mainstream recurrent neural time series prediction models are used as comparison models. Through simulation experiments, the current number of remaining infections in the epidemic data of China, America and Britain is predicted in the short term, and RMSE and MAE are used as evaluation indicators, and then the best model is selected for medium ? and long?term prediction. The experimental results show that the indicator value of the informer model is optimal in both RMSE and MAE, further indicating that the prediction accuracy of the informer model is higher than that of other comparative models in China, America and Britain. Finally, the Informer model is used for the development of the epidemic in China,America and Britain medium and long?term prediction.

Key words: COVID?19, Threshold autoregressive (TAR), Long short?term memory (LSTM), Convolutional long short?term memory (ConvLSTM), Gated recurrent unit (GRU), Temporal convolutional network (TCN), Informer algorithm

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引用本文

常万杰, 刘琳琳, 曹宇, 曹杨, 魏海平. 基于Informer算法的病毒传播预测研究[J]. 辽宁石油化工大学学报, 2024, 44(1): 80-88.

Wanjie CHANG, Linlin LIU, Yu CAO, Yang CAO, Haiping WEI. Research on Virus Propagation Prediction Based on Informer Algorithm[J]. Journal of Liaoning Petrochemical University, 2024, 44(1): 80-88.

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链接本文: http://journal.lnpu.edu.cn/CN/10.12422/j.issn.1672-6952.2024.01.012

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