Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 199-207.DOI: 10.11772/j.issn.1001-9081.2020060877

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles     Next Articles

Prediction method on financial time series data based on matrix profile

GAO Shile1, WANG Ying1, LI Hailin1,2, WAN Xiaoji1   

  1. 1. Business School, Huaqiao University, Quanzhou Fujian 362021, China;
    2. Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen Fujian 361021, China
  • Received:2020-05-31 Revised:2020-07-04 Online:2021-01-10 Published:2020-11-12
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (71771094), the Natural Science Foundation of Fujian Province (2019J01067).


高世乐1, 王滢1, 李海林1,2, 万校基1   

  1. 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
  • 通讯作者: 李海林
  • 作者简介:高世乐(1972-),男,黑龙江绥化人,讲师,博士,主要研究方向:数据挖掘、复杂网络;王滢(1997-),女,福建宁德人,主要研究方向:时间序列数据挖掘;李海林(1982-),男,福建龙岩人,教授,博士,CCF会员,主要研究方向:数据挖掘、商务分析;万校基(1984-),男,江西南昌人,讲师,博士,主要研究方向:数据挖掘、金融分析。
  • 基金资助:

Abstract: 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.

Key words: institutional trading behavior, stock trend prediction, motif discovery, Matrix Profile (MP), time series

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。

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

CLC Number: