计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 125-130.doi: 10.11896/jsjkx.200700050
所属专题： 大数据&数据科学 虚拟专题
刘翀, 杜军平
LIU Chong, DU Jun-ping
摘要： 随着互联网的迅速发展金融市场每日产生了大量在线金融数据如每日的交易次数以及交易的总金额等.近年来金融市场数据的动态预测成为了研究热点.金融市场数据量大输入序列较多且会随着时间发生变化.针对这些问题文中提出了基于深度LSTM和注意力机制的金融数据预测模型.首先该模型能处理复杂的金融市场数据输入主要是多序列的输入;其次该模型使用深度LSTM网络对金融数据进行建模解决了数据间长依赖的问题并能学习到更加复杂的市场动态特征;最后该模型引入了注意力机制使得不同时间的数据对预测的重要程度不同预测更加精准.在真实的金融大数据集上的实验表明所提模型在动态预测领域具有准确性高、稳定性好的特点.
中图分类号:
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