Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting
作者：
Highlights：
• A novel net named Auto-Regressive Time Delayed (ARTiDe) jump neural network is presented.
• It is tested on blood glucose levels prediction using 24 h of monitoring data.
• The model exploits a univariate approach reducing the burden of data collection.
• The model outperforms many multivariate approaches at the state of the art.
• A thorough overview of glucose levels forecasting is provided.
摘要
•A novel net named Auto-Regressive Time Delayed (ARTiDe) jump neural network is presented.•It is tested on blood glucose levels prediction using 24 h of monitoring data.•The model exploits a univariate approach reducing the burden of data collection.•The model outperforms many multivariate approaches at the state of the art.•A thorough overview of glucose levels forecasting is provided.
论文关键词：Time series forecasting,Neural networks,Precision medicine,Diabetes,Bio-medical patterns
论文评审过程：Received 19 December 2019, Revised 6 June 2020, Accepted 9 June 2020, Available online 12 June 2020, Version of Record 17 June 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106134
