Academic Journal of Computing & Information Science, 2024, 7(4); doi: 10.25236/AJCIS.2024.070411.
Qinzheng Hu1, Yunchao He2, Chengrunyan Yang3, Haoyan Zhang4, Shengjin Liu5
1International Education School, Suzhou University of Science and Technology, Suzhou, China
2Xi’an Jiaotong-Liverpool University, Suzhou, China
3College of Science, Civil Aviation University of China, Tianjin, China
4Beijing Jingshan School, Beijing, China
5School of Medical Information Engineering, Shandong First Medical University, Tai'an, China
Because the LSTM neural network model and GRU neural network model have better advantages in forecasting compared with other neural network models, this paper uses the LSTM model and GRU model to build eight different ensemble neural network models to predict stock prices. The errors of the prediction results will be analyzed by using mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) as the error measurement standards. A comparison will be made to determine the ensemble model with the smallest error and the highest precision.
Stock price prediction, LSTM, GRU, ensemble model
Qinzheng Hu, Yunchao He, Chengrunyan Yang, Haoyan Zhang, Shengjin Liu. Stock price prediction based on deep learning. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 4: 80-89. https://doi.org/10.25236/AJCIS.2024.070411.
[1] Guś and J. Możaryn, "Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay," 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, 2023, pp. 111-116, doi: 10.1109/MMAR58394. 2023. 10242473.
[2] Yani Hou. Stock Price Prediction Based on Bi-LSTM Deep Learning [J]. Statistics and Application, 2021, 10(3): 538-546.
[3] Chaobin Huang, Ximing Cheng. Research on stock price prediction based on LSTM neural network [J]. Journal of Beijing Information Science & Technology University, 2021, 36(1): 79-83.
[4] Zhen Liu, Huimin Wang, Siyv Hua, Yv Chen. Research on stock price prediction based on deep learning [J]. Science and Technology Innovation Herald, 2018, 13(247): 247-248.
[5] Ruixue Zhang, Yongtao Hao. Research on stock price prediction based on deep learning [J]. Computer Knowledge and Technology, 2023, 19(33):8-10. DOI:10.14004/j.cnki.ckt.2023.1739.
[6] Di Zhang. Research on Data Mining and Prediction Model Based on KNN and Neural Network Algorithm [J]. Journal of Taiyuan Normal University(Natural Science Edition),2023,22(02):29-34.
[7] Wenquan Cui, Qingfang Wang. Stock price prediction based on dual encoder using online social network information [J].Journal Of University Of Science And Technology Of China, 2020, 50(08):1093-1101.
[8] Shimin Huang. Stock price analysis and prediction based on ARIMA model -- taking China Merchants Bank as an example [J]. Management & Technology of SME, 2022, (11):184-187.
[9] Jinlei Han, Pingping Xiong, Jihong Sun. Stock price time series forecasting based on LSTM and grey model [J].Journal of Nanjing University of Information Science & Technology,2023,15(06):631-642.DOI:10.13878/j.cnki.jnuist.20221008002.
[10] Xiaojie Li, Huan Xia. Research on stock price regression prediction based on machine learning algorithm [J]. Science & Technology Information, 2023, 21(14):227-231. DOI:10.16661/j.cnki.1672-3791. 2211-5042-9348.
[11] Meng Li, Zhangjie Huang, Jianhui Xv. Stock price prediction based on LSTM-Wavelet model based on deep learning and Wavelet analysis [J]. J Chongqing Technol & Business Univ (Nat Sci Ed), 2023, 40(02):99-105. DOI:10.16055/j.issn.1672-058X.2023.0002.015.