基于DMD-LSTM模型的股票价格时间序列预测研究

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-03 Cooperative journals: 《计算机应用研究》

史建楠 邹俊忠 张见 汪春梅 卫作臣

Abstract： Aiming at the problems of low prediction accuracy and feature extraction difficulty in complicated stock market, this paper proposed a stock price prediction method based on dynamic mode decomposition and long short-term memory neural network (DMD-LSTM) . Firstly, it used the DMD algorithm to decompose the industry specific stock in the background of plate linkage phenomenon, and extracted the mode feature which included stock trend information. Then, built the LSTM network to establish the relations between stock price and the feature of mode and basic index in different market conditions. The experimental results on Angang Steel (sh600000) show that, the proposed method has the higher forecasting precision compared with the traditional ways in specific condition, which can characterize the trend of stock price changes better .

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