Wang Benfeng, Shen Yuqi, Yan Xiaoran, Kong Xiangjie
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
The Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, Zhejiang, China.
PeerJ Comput Sci. 2024 May 1;10:e2046. doi: 10.7717/peerj-cs.2046. eCollection 2024.
The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.
新冠疫情对全球经济和公共卫生产生了深远影响。为防止疫情再次爆发，开发短期预测模型至关重要。我们提出一种用于预测未来病例的ARIMA-LSTM（自回归积分移动平均和长短期记忆）模型，并利用多源数据来提高预测性能。首先，我们使用ARIMA-LSTM模型分别预测多源数据的发展趋势。随后，我们引入贝叶斯注意力机制，将辅助数据源的预测结果整合到病例数据中。最后，基于真实数据集进行实验。结果表明预测病例数与实际病例数密切相关，该模型的预测性能优于基线方法和其他现有最先进方法。
