计算机应用 ›› 2015, Vol. 35 ›› Issue (8): 2397-2403.DOI: 10.11772/j.issn.1001-9081.2015.08.2397
收稿日期:
2015-03-01
修回日期:
2015-04-17
出版日期:
2015-08-10
发布日期:
2015-08-14
通讯作者:
高峰(1967-),男,陕西西安人,教授,博士生导师,博士,主要研究方向:机器学习、系统优化调度,fgao@sei.xjtu.edu.cn
作者简介:
李丰(1988-),女,辽宁绥中人,硕士研究生,主要研究方向:数据挖掘、机器学习; 寇鹏(1983-),男,陕西西安人,讲师,博士,主要研究方向:新能源系统预测与优化控制、电机及拖动系统优化控制、人工智能、机器学习。
基金资助:
国家自然科学基金资助项目(61221063,U1301254,61473218);国家863计划项目(2012AA011003);国家"111引智计划"项目。
LI Feng1, GAO Feng1, KOU Peng2
Received:
2015-03-01
Revised:
2015-04-17
Online:
2015-08-10
Published:
2015-08-14
摘要:
针对股票交易过程中价格转折点的预测问题,提出了一种基于分段线性表示(PLR)与高斯过程分类(GPC)相结合的股票价格转折点预测算法PLR-GPC。该算法通过PLR提取股票历史价格序列的转折点,对转折点进行分类标记,建立基于GPC的股票价格转折点预测模型,以上述股票历史价格序列对模型进行训练,最终由预测模型对股票价格转折点进行预测,并对预测结果进行概率解释。将PLR-GPC与基于BP神经网络(BPN)的PLR-BPN算法、基于加权支持向量机支持向量机(WSVM)的PLR-WSVM算法进行实验对比:PLR-GPC在预测准确率上高于PLR-BPN与PLR-WSVM;在投资收益率上高于PLR-BPN,与PLR-WSVM持平。实验结果表明PLR-GPC在股票价格转折点的预测上是有效的,并且可以应用在实际股票投资交易中。
中图分类号:
李丰, 高峰, 寇鹏. 基于分段线性表示和高斯过程分类的股票转折点概率预测[J]. 计算机应用, 2015, 35(8): 2397-2403.
LI Feng, GAO Feng, KOU Peng. Integrating piecewise linear representation and Gaussian process classification for stock turning points prediction[J]. Journal of Computer Applications, 2015, 35(8): 2397-2403.
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