基于K-means-LSTM模型的证券股价预测  

Securities Stock Price Prediction Based on K-means-LSTM Model

作　　者：肖田田 XIAO Tiantian(School of Mathematics and Science,Anhui Jianzhu University,Hefei 230601,China)

机构地区：[1]安徽建筑大学数理学院,合肥230601

出　　处：《科技和产业》2024年第3期210-215,共6页Science Technology and Industry

基　　金：安徽省高校自然科学研究项目(KJ2020A0479);安徽建筑大学博士启动基金(2020QDZ20)。

摘　　要：鉴于股票数据具有非平稳、非线性等特征,传统的统计模型无法精准预测股票价格的未来趋势。针对这个问题,构建一种混合深度学习方法来提高股票预测性能。首先,通过将距离算法修改为DTW(动态时间归整),令K-means聚类算法拓展为更适用于时间序列数据的K-means-DTW,聚类出价格趋势相似的证券;然后,通过聚类数据来训练LSTM(长短时记忆网络)模型,以实现对单支股票价格的预测。实验结果表明,混合模型K-means-LSTM表现出更好的预测性能,其预测精度和稳定性均优于单一LSTM模型。In view of the non-stationary and non-linear characteristics of stock data,traditional statistical models cannot accurately predict the future trend of stock prices.To address this problem,a hybrid deep learning method is constructed to improve prediction performance.Firstly,the distance algorithm is modified to DTW(dynamic time warping)by expanding the K-means clustering algorithm to K-means-DTW,which is more suitable for time series data,to cluster securities with similar price trends.Then,the LSTM(long short-term memory)model is trained through clustering data to predict the price of a single stock.Experimental results show that the hybrid model K-means-LSTM shows better prediction performance and its prediction accuracy and stability are better than the single LSTM model.

关 键 词：股票价格预测 K-MEANS DTW(动态时间归整) K-means-LSTM(K均值-长短时记忆网络)混合模型 

分 类 号：F830[经济管理—金融学]