A CNN-LSTM-Based Model to Forecast Stock Prices
Jiangsu Second Normal University · Hebei University of Science and Technology
Abstract
Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price,…
Citation impact
- FWCI
- 44.99
- Percentile
- 100%
- References
- 33
Authors
5- WLWenjie Lu
Jiangsu Second Normal University, Hebei University of Science and Technology
- JLJiazheng Li
Hebei University of Science and Technology
- YLYifan Li
Hebei University of Science and Technology
- SASun AijunCorresponding
Jiangsu Second Normal University
- JWJingyang Wang
Hebei University of Science and Technology
Topics & keywords
- Computer science
- Stock price
- Stock (firearms)
- Time series
- Econometrics
- Artificial intelligence
- Machine learning
- Series (stratigraphy)