Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

作者：

Highlights：

• Feature selection based multivariate time series forecasting makes possible the generation of precise models for antibiotic resistance outbreak prediction.

• Antibiotic resistance outbreak prediction contributes to the improvement of the surveillance systems and, therefore, their prevention.

• Multiobjective decision support techniques are appropriate for the identification of accurate and robust multivariate time series forecasting models.

摘要

•Feature selection based multivariate time series forecasting makes possible the generation of precise models for antibiotic resistance outbreak prediction.•Antibiotic resistance outbreak prediction contributes to the improvement of the surveillance systems and, therefore, their prevention.•Multiobjective decision support techniques are appropriate for the identification of accurate and robust multivariate time series forecasting models.

论文关键词：Feature selection,Multi-objective evolutionary algorithms,Multivariate time series,Antibiotic resistance forecasting,Multiple criteria decision making

论文评审过程：Received 7 August 2019, Revised 2 February 2020, Accepted 6 February 2020, Available online 19 February 2020, Version of Record 21 April 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.101818