基于概率后缀树的股票时间序列预测方法研究OA北大核心CSCDCSTPCD
Research of stock time series based on probabilistic suffix tree
在时间序列符号化基础上,本文引入概率后缀树PST模型,构建基于时间序列符号化和概率后缀树相结合的股票预测模型.本文选择在沪深300的10支股票数据上将预测模型与传统的马尔科夫模型MM和自回归移动平均模型ARMA进行对比,结果显示本文提出的股票预测模型优于MM模型和ARMA模型,验证了本文所提出的预测模型在投资收益上的有效性.
A stock forecasting model was introduced in this paper ,which was based on the combination of time series symbolization and Probabilistic Suffix Tree (PST ) .In addition ,the Markov Model (MM ) and the Auto Regressive Moving Average Model (ARMA) was compared with the forecasting model of this paper .The stock of 10 CSI 300 indices was used as the experimental sample .The results show that the stock forecasting model proposed in this paper is better than the …查看全部>>
程小林;郑兴;李旭伟
四川大学计算机学院 ,成都 610065四川大学计算机学院 ,成都 610065四川大学计算机学院 ,成都 610065
信息技术与安全科学
股票数据挖掘时间序列符号化高斯混合模型聚类概率后缀树
Stock data miningTime series symbolizationGaussian mixture modelingProbabilistic suf-fix tree
《四川大学学报（自然科学版）》 2018 (1)
时态序列中的信息转移挖掘研究
61-66,6
国家自然科学基金(61173099)
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