Multi-step influenza outbreak forecasting using deep LSTM network and genetic algorithm

作者：

Highlights：

• The GA-LSTM model is proposed for the multi-step influenza outbreak forecasting.

• GA is utilized to acquire the optimum hyperparameters of LSTM network architecture.

• The GA-LSTM model outperforms the state-of-the-art approaches.

• MAE and RMSE of GA-LSTM improved by about 6.96% and 5.14% compared to the FCNN.

摘要

•The GA-LSTM model is proposed for the multi-step influenza outbreak forecasting.•GA is utilized to acquire the optimum hyperparameters of LSTM network architecture.•The GA-LSTM model outperforms the state-of-the-art approaches.•MAE and RMSE of GA-LSTM improved by about 6.96% and 5.14% compared to the FCNN.

论文关键词：Deep neural network,Influenza outbreak prediction,Long short-term memory,Genetic algorithm,Multi-step forecasting

论文评审过程：Received 8 March 2020, Revised 30 April 2021, Accepted 30 April 2021, Available online 5 May 2021, Version of Record 13 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115153