摘要
现有K线模式主要是通过人工观察方式(即人工相似性搜索)获得的。针对现有K线模式在股票预测中预测效果一般,且部分学者否认K线模式具有预测能力的现状,采用计算机技术和数据挖掘等方法,重新对K线序列相似性搜索预测进行研究。首先,定义K线序列的相似性度量模型,包括K线序列的形态相似性和位置相似性,来解决K线序列的相似性匹配问题;接着,基于K线序列的相似性度量模型,定义K线滑动搜索算法,来解决K线序列的相似性搜索问题;最后,基于K线序列的相似性搜索结果,提出了两种股票价格预测方法:普通序列相似性搜索预测法和模式序列相似性搜索预测法。在实验中,普通序列和模式序列两种方法的预测准确率分别可以达到72.5%和77.8%。实验结果表明,K线模式具有预测能力,且K线模式较普通序列的预测效果更好;提出的两种股票预测方法,均可以较好地应用于股票预测与投资。
The existing K-line patterns are acquired by artificial observation, i. e., artificial similarity search. Hence,there are a series of problems that the forecast performance of these patterns is actually modest, and some researchers even deny their possibility. Based on these problems, the restudy of K-line series prediction based on similarity search was presented using the methods of computer technology and data mining. Firstly, the similarity measure model was defined to solve the similarity match problem of K-line series, including the shape similarity model and the position similarity model.Secondly, based on the similarity measure model, the K-line sliding search algorithm was proposed to resolve the problem of K-line series＇ similarity search. Finally, based on the similarity search results, two stock prediction methods were presented,which are common series based similarity search and pattern series based similarity search. In the experiment, the forecast accuracies of the methods of common series and K-line patterns can reach to 72. 5% and 77. 8%, respectively. The experimental results show that, the K-line patterns do have predictive ability, and its predictive performance is better than common series. The proposed two prediction methods could be well applied in stock prediction and investment.
出处
《计算机应用》
CSCD
北大核心
2017年第A02期229-235,共7页
journal of Computer Applications
基金
"十二五"国家科技支撑计划项目(2015BAF10B01)
上海市科委基础研究项目(14JC1402203)
关键词
股票预测
K线图
K线序列
K线模式
相似性匹配
相似性搜索
stock prediction
K-line chart
K-line series
K-line pattern
similarity match
similarity search
