摘要
面对越来越复杂的数据环境,以经典统计学模型为主的股票预测模型在一定程度上已无法满足人们对预测准确性的要求。深度学习因具有较强的学习能力和抗干扰能力,已逐渐被应用于股票推荐中。但传统的股票推荐模型要么从未考虑时间因素,要么仅考虑时间上的单向关系。因此,文中提出了一种基于深度双向LSTM的神经网络预测模型。该模型充分利用了时间序列上向前、向后两个时间方向的上下文关系,解决了长时间序列上的梯度消失和梯度爆炸问题,能够学习到对时间有长期依赖性的信息。同时,该模型引入了Dropout策略,在一定程度上解决了深层网络模型带来的训练难、收敛速度慢和过拟合等问题。在S&P500数据集上的实验表明,基于深度双向LSTM的神经网络预测模型比现有预测模型在误差上降低了2%~5%,使决定系数(r2)提高了10%。
With the diversity of applications scenarios and rapid growth of data,the stock prediction models based on classical statistical methods are unable to meet the requirements for high prediction accuracy.But traditional stock reco- mmendation models either never consider the time factor or just consider the unidirectional relationship over time.However,existing stock recommendation models based on deep learning rarely consider the time factor.This paper proposed a deep bidirectional LSTM model for stock prediction,which makes full use of context relationship in the forward direction and backward direction of time series.The problem of vanishing gradient and exploding gradient are solved by introducing LSTM when dealing with long-term sequence.The proposed model can learn information which has long-term dependence on time.At the same time,dropout strategy is introduced to prevent over-fitting caused by deep network model and speed up the training.Experiments on S&P500 dataset show that the neural network prediction model based on the deep bidirectional LSTM outperforms the existing prediction models,the error is about 5% lower,and the coefficient of determination ( r 2) is increased by 10%.
作者
曾安
聂文俊
ZENG An;NIE Wen-jun(School of Computer,Guangdong University of Technology,Guangzhou 510006,China;Guangdong Key Laboratory of Big Data Analysis and Processing,Guangzhou 510006,China)
出处
《计算机科学》
CSCD
北大核心
2019年第10期84-89,共6页
Computer Science
基金
国家自然科学基金项目(61772143,61300107)
广东省自然科学基金项目(S2012010010212)
广州市科技计划项目(201601010034,201505031501397)
广东省大数据分析与处理重点实验室开放基金项目(201801)资助
关键词
推荐系统
股票预测
深度RNN
双向LSTM
Recommendation system
Stock forecast
Deep recurrent neural networks
Bidirectional long short-term memory
