计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 491-495.doi: 10.11896/jsjkx.200100055
王婷, 夏阳雨新, 陈铁明
WANG Ting, XIA Yang-yu-xin, CHEN Tie-ming
摘要: 随着经济和科技的快速发展,股市已成为当前金融市场的重要组成部分。传统机器学习方法在处理非线性、高噪声、波动性强的股票时序预测问题时存在局限性,而近年来深度神经网络的兴起,给股票趋势预测问题提供了新的解决方案。采用长短期记忆网络(Long Short-Term Memory,LSTM)来处理长距离的股票时序问题,构建了一个多类别特征体系作为长短期记忆网络的输入进行训练,包括常用技术指标、多种关键转折点特征和个股真实事件信息等。同时,通过实验全面分析了各类特征对股票趋势预测的有效程度,对比结果表明了多类别特征体系在预测中的良好表现,其能够达到68.77%的短期涨跌预测准确率。另外还将LSTM与CNN,RNN和MLP等模型进行了比较,实验结果表明LSTM在解决该时序预测问题上优于其他模型。
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