Research on Stock Price Prediction Based on CNN-LSTM Combined Model

Shangzhi Gao

doi:10.23977/acss.2023.070910

Research on Stock Price Prediction Based on CNN-LSTM Combined Model

Download as PDF

DOI: 10.23977/acss.2023.070910 | Downloads: 31 | Views: 293

Author(s)

Shangzhi Gao ¹

Affiliation(s)

¹ Dalian Minzu University, Dalian, Liaoning, 116650, China

Corresponding Author

Shangzhi Gao

ABSTRACT

It is difficult to forecast time series data such as stock prices because of the complexity of the internal structure of the stock price system and the diversity of external factors that determine the complexity of the stock market and the uncertainty of the stock price forecasting task. In addition, the impact of different factors on stock prices may be linear or non-linear. Therefore, this paper proposes a combined CNN-LSTM model with good results and generalization ability, which can fully exploit the advantages of each model in stock price prediction and improve the accuracy and stability of prediction. This research can improve the effectiveness of stock price prediction by constructing suitable model structures, parameter configurations and optimization algorithms to provide investors and financial practitioners with a powerful reference for decision-making. An attention mechanism is also introduced in order to improve the prediction accuracy of the CNN-LSTM model, and the results confirm the efficacy of the proposed approach.

KEYWORDS

CNN-LSTM; Deep Learning; Predictive model

CITE THIS PAPER

Shangzhi Gao, Research on Stock Price Prediction Based on CNN-LSTM Combined Model. Advances in Computer, Signals and Systems (2023) Vol. 7: 73-79. DOI: http://dx.doi.org/10.23977/acss.2023.070910.

REFERENCES

[1] Bao W, Yue J, Rao Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory [J]. PloS one, 2017, 12(7): e0180944.
[2] Han Shanjie, Tan Shizhe. Design and implementation of a deep learning model for stock prediction based on Tensor Flow [J]. Computer Applications and Software, 2018, 35(6): 267-271.
[3] Li M. Research on stock market prediction based on deep learning [D]. Nanjing University of Posts and Telecommunications, 2019. doi:10.27251/d.cnki.gnjdc.2019.000946.
[4] Zhang Zeya, Huang Liming, Chen Chong et al. A stock prediction method based on news feature extraction and recurrent neural networks[J]. Journal of Literature and Data, 2020, 2(01):45-56.
[5] Gao W. Modeling stock market using new hybrid intelligent method based on MFNN and IBHA[J]. Soft Computing, 2022, 26(15): 7317-7337.
[6] Su XN. Data warehousing and data mining [M]. Tsinghua University Press Limited, 2006.
[7] Wang Min, Lu Xikun, Zhou Yidi. Gun Life Prediction Model Based on Bayesian Optimization CNN-LSTM. Integrated Ferroelectrics, 2022.
[8] Teng ZW, Zhou JH. Anomalous cell detection based on variational self-encoder[J]. Mobile Communication, 2020.

Subscription

E-Mail Alert

Downloads:	13349
Visits:	257580

Research on Stock Price Prediction Based on CNN-LSTM Combined Model

Author(s)

Affiliation(s)

Corresponding Author

ABSTRACT

KEYWORDS

CITE THIS PAPER

REFERENCES

RESOURCES

JOIN US

PUBLICATION SERVICES

CONTACT US