Open AccessResearch of Stock Price Prediction Based on PCA-LSTM Model
Author(s) -
Yulian Wen,
Peng Lin,
Xiushan Nie
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012109
Subject(s) - principal component analysis , stock price , stock (firearms) , econometrics , computer science , stock market , artificial intelligence , data mining , machine learning , economics , series (stratigraphy) , engineering , mechanical engineering , paleontology , horse , biology
At present, there are some problems in domestic stock market, such as difficulty in extracting effective features and inaccuracy in stock price forecast. This paper proposes a stock price prediction model based on Principal Component Analysis (PCA) and Long Short-Term Memory (LSTM). Firstly, PCA is used to extract the principal components of the technical indicators affecting stock prices, so as to reduce the data correlation and realize data dimensionality reduction. Then, LSTM is used to model and predict the stock price. According to the experimental results of Pingan Bank, compared with the traditional stock price prediction models, the stock price prediction model based on PCA and LSTM can accurately predict the stock price fluctuation trend.