MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||6|
|Section||3D Images Reconstruction and Virtual System|
|Published online||19 November 2018|
Research Based on Stock Predicting Model of Neural Networks Ensemble Learning
School of information science and engineering, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China
2 Ecole Supérieure de Commerce, Grenoble Ecole de Management, Grenoble, 38000, France
3 School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei, 430072, China
a Corresponding author: firstname.lastname@example.org
Financial time series is always one of the focus of financial market analysis and research. In recent years, with the rapid development of artificial intelligence, machine learning and financial market are more and more closely linked. Artificial neural network is usually used to analyze and predict financial time series. Based on deep learning, six layer long short-term memory neural networks were constructed. Eight long short-term memory neural networks were combined with Bagging method in ensemble learning and predicting model of neural networks ensemble learning was used in Chinese Stock Market. The experiment tested 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. For long short-term memory neural network ensemble learning model, its accuracy is 58.5%, precision is 58.33%, recall is 73.5%, F1 value is 64.5%, and AUC value is 57.67%, which are better than those of multilayer long short-term memory neural network model and reflect a good prediction outcome.
© The Authors, published by EDP Sciences, 2018
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