基于矩阵画像的金融时序数据预测方法  

Prediction method on financial time series data based on matrix profile

作　　者：高世乐[1] 王滢 李海林[1,2] 万校基[1] GAO Shile;WANG Ying;LI Hailin;WAN Xiaoji(Business School,Huaqiao University,Quanzhou Fujian 362021,China;Research Center for Applied Statistics and Big Data,Huaqiao University,Xiamen Fujian 361021,China)

机构地区：[1]华侨大学工商管理学院,福建泉州362021 [2]华侨大学应用统计与大数据研究中心,福建厦门361021

出　　处：《计算机应用》2021年第1期199-207,共9页journal of Computer Applications

基　　金：国家自然科学基金资助项目(71771094);福建省自然科学基金资助项目(2019J01067)。

摘　　要：针对金融市场中机构交易对股票市场中的散户投资行为具有较强的误导性的现象,提出了一种基于机构交易行为影响的趋势预测方法。首先,利用时间序列的矩阵画像(MP)方法,以股票换手率数据为切入点,构建不同兴趣模式长度下的基于机构交易行为影响的换手率波动知识库;其次,确定待预测股票在兴趣模式长度取何值时的预测结果精确度高;最后,根据该兴趣模式长度下的知识库,预测在机构交易行为影响下的单支股票的波动趋势。为验证趋势预测新方法的可行性和准确性,将其与自回归滑动平均(ARMA)模型和长短时记忆(LSTM)网络这两种预测方法进行对比分析,运用均方根误差(RMSE)与平均绝对百分误差(MAPE)评价指标综合比较3种方法对70支股票的预测结果。实验结果分析表明,与ARMA模型和LSTM网络相比,在70支的股票价格趋势预测上,所提方法有80%以上的股票预测结果更准确。For the fact that institutional trading in the financial market is highly misleading to retail investors in the financial market,a trend prediction method based on the impact of institutional trading behaviors was proposed.First,using the time series Matrix Profile(MP) algorithm and taking the stock turnover rate as the cut-in point,a knowledge base of turnover rate fluctuations based on the influence of institutional trading behaviors under motifs with different lengths was constructed.Second,the motif’s length,which leads to the high accuracy of the prediction result of the stock to be predicted was determined.Finally,the fluctuation trend of single stock under the influence of institutional trading behaviors was predicted through the knowledge base of this motif’s length.In order to verify the feasibility and accuracy of the new method of trend prediction,the method was compared with Auto-Regressive Moving Average(ARMA) model and Long Short Term Memory(LSTM) network,and the Root-Mean-Square Error(RMSE) and Mean Absolute Percentage Error(MAPE)evaluation indicators were used to compare the 70 stocks’ prediction results of three methods.The analysis of experimental results show that,compared with the ARMA model and the LSTM network,in the prediction of 70 stock price trends,the proposed method has more than 80% of the stock prediction results more accurate.

关 键 词：机构交易行为 股票趋势预测 兴趣模式发现 矩阵画像 时间序列 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]