计算机科学 ›› 2019, Vol. 46 ›› Issue (10): 84-89.doi: 10.11896/jsjkx.180901771
曾安1, 聂文俊2
ZENG An1, NIE Wen-jun2
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%。
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