A CNN-LSTM-Based Model to Forecast Stock Prices

Citations

Cited by:

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3. Esteban Vanegas & Andrés Mora-Valencia, 2025. "Correction: Skew Index: a machine learning forecasting approach," Risk Management, Palgrave Macmillan, vol. 27(3), pages 1-1, September.
4. Shao, Qihui & Du, Yongqiang & Xue, Wenxuan & Yang, Zhiyuan & Jia, Zhenxin & Shao, Xianzhu & Xu, Xue & Duan, Hongbo & Zhu, Zhipeng, 2024. "Predicting China's thermal coal price: Does multivariate decomposition-integrated forecasting model with window rolling work?," Resources Policy, Elsevier, vol. 99(C).
5. Yeh, Wei-Chang & Du, Chia-Ming & Tan, Shi-Yi & Forghani-elahabad, Majid, 2023. "Application of LSTM based on the BAT-MCS for binary-state network approximated time-dependent reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
6. Cheng, Wei & Wang, Yan & Peng, Zheng & Ren, Xiaodong & Shuai, Yubei & Zang, Shengyin & Liu, Hao & Cheng, Hao & Wu, Jiagui, 2021. "High-efficiency chaotic time series prediction based on time convolution neural network," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
7. Yazdan Babazadeh Maghsoodlo & Daniel Dylewsky & Madhur Anand & Chris T. Bauch, 2025. "Deep Learning for Bifurcation Detection: Extending Early Warning Signals to Dynamical Systems with Coloured Noise," Mathematics, MDPI, vol. 13(17), pages 1-20, August.
8. Li, Yan-Fu & Zhao, Wei & Zhang, Chen & Ye, Jiantao & He, Huiru, 2024. "A study on the prediction of service reliability of wireless telecommunication system via distribution regression," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
9. Ding, Lili & Zhang, Rui & Zhao, Xin, 2024. "Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks," Energy, Elsevier, vol. 288(C).
10. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
11. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
12. Shrey Jain & Camille Bruckmann & Chase McDougall, 2022. "NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks," Papers 2204.12932, arXiv.org.
13. Heon Baek & Eui-Bang Lee, 2025. "Feature Expansion Effect Approach for Improving Stock Price Prediction Performance," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 2029-2054, September.
14. Mingfu Xue & Junyu Zhu & Rusheng Wu & Xiayiwei Zhang & Yuan Chen, 2024. "BRP-Net: A discrete-aware network based on attention mechanisms and LSTM for birth rate prediction in prefecture-level cities," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-22, September.
15. Miao, Hua & Zhu, Wei & Dan, Yuanhong & Yu, Nanxiang, 2024. "Chaotic time series prediction based on multi-scale attention in a multi-agent environment," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
16. Yuhao Zhou & Ruijie Wang & An Zeng, 2022. "Predicting the impact and publication date of individual scientists’ future papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1867-1882, April.
17. Parisa Foroutan & Salim Lahmiri, 2024. "Deep learning systems for forecasting the prices of crude oil and precious metals," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.
18. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
19. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
20. Yang, Jiahao & Fang, Ran & Zhang, Ming & Zhang, Wenkai & Zhou, Jun, 2025. "Enhancing stock ranking forecasting by modeling returns with heteroscedastic Gaussian Distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
21. Qizhao Chen & Hiroaki Kawashima, 2025. "Adaptive Alpha Weighting with PPO: Enhancing Prompt-Based LLM-Generated Alphas in Quant Trading," Papers 2509.01393, arXiv.org.
22. Esteban Vanegas & Andrés Mora-Valencia, 2025. "Skew Index: a machine learning forecasting approach," Risk Management, Palgrave Macmillan, vol. 27(1), pages 1-60, February.
23. Akanksha Sharma & Chandan Kumar Verma & Priya Singh, 2025. "Enhancing Option Pricing Accuracy in the Indian Market: A CNN-BiLSTM Approach," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3751-3778, June.
24. Hongfei Xiao, 2025. "Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-34, June.
25. Saeed, Adnan & Li, Chaoshun & Gan, Zhenhao, 2024. "Short-term wind speed interval prediction using improved quality-driven loss based gated multi-scale convolutional sequence model," Energy, Elsevier, vol. 300(C).
26. Lin, Yong & Wang, Renyu & Gong, Xingyue & Jia, Guozhu, 2022. "Cross-correlation and forecast impact of public attention on USD/CNY exchange rate: Evidence from Baidu Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).