A novel data-driven stock price trend prediction system
作者：
Highlights：
• A data-driven stock price trend prediction system is designed and implemented.
• Models are trained from historical data using random forest with feature selection.
• Training data are created by unsupervised morphological pattern recognition.
摘要
•A data-driven stock price trend prediction system is designed and implemented.•Models are trained from historical data using random forest with feature selection.•Training data are created by unsupervised morphological pattern recognition.
论文关键词：Feature selection,Morphological pattern recognition,Random forest,Stock price prediction
论文评审过程：Received 8 September 2017, Revised 12 December 2017, Accepted 13 December 2017, Available online 13 December 2017, Version of Record 20 December 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.12.026
