计算机工程与应用2019，Vol.55Issue(8)：238-243,6.DOI:10.3778/j.issn.1002-8331.1807-0294
基于神经网络集成学习股票预测模型的研究
Research Based on Stock Predicting Model of Neural Networks Ensemble Learning
摘要
Abstract
Based on deep learning, six layer long short-term memory neural networks are constructed. Eight long short-term memory neural networks are combined with bagging method in ensemble learning and predicting model of neural networks ensemble learning is used in Chinese Stock Market. The experiment tests Shanghai Composite Index, Shenzhen Composite Index, Shanghai Stock Exchange 50 Index, Shanghai-Shenzhen 300 Index, Medium and Small Plate Index and Gem Index during the period from January 4, 2012 to December 29, 2017. The model’s accuracy is 58.5%, precision is 58.33%, recall is 73.5%, F1 value is 64.5%, and AUC value is 57.67%, which reflect a good prediction outcome.关键词
长短记忆神经网络/装袋算法/股票/准确率/精确率Key words
Long Short-Term Memory(LSTM)/ bagging/ stock/ accuracy/ precision分类
信息技术与安全科学引用本文复制引用
谢琪,程耕国,徐旭..基于神经网络集成学习股票预测模型的研究[J].计算机工程与应用,2019,55(8):238-243,6.基金项目
江苏省自然科学基金(No.BK20151463) （No.BK20151463）
国家自然科学基金(No.51505213,No.61104085) （No.51505213,No.61104085）
江苏省高校自然科学重大项目(No.14KJA460003) （No.14KJA460003）
江苏省产学研合作前瞻性项目(No.BY2016008-07) （No.BY2016008-07）
南京工程学院自然科学基金(No.CKJB201702,No.ZKJ201508). （No.CKJB201702,No.ZKJ201508）
