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Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data

Citations

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Cited by:

  1. Ling Qi & Matloob Khushi & Josiah Poon, 2021. "Event-Driven LSTM For Forex Price Prediction," Papers 2102.01499, arXiv.org.
  2. Zhiyuan Pei & Jianqi Yan & Jin Yan & Bailing Yang & Ziyuan Li & Lin Zhang & Xin Liu & Yang Zhang, 2024. "A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images," Papers 2410.19291, arXiv.org, revised Oct 2024.
  3. Huang, Wenyang & Wang, Huiwen & Qin, Haotong & Wei, Yigang & Chevallier, Julien, 2022. "Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method," Energy Economics, Elsevier, vol. 110(C).
  4. Catalin Stoean & Wiesław Paja & Ruxandra Stoean & Adrian Sandita, 2019. "Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
  5. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
  6. Antonello Rosato & Rodolfo Araneo & Amedeo Andreotti & Federico Succetti & Massimo Panella, 2021. "2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series," Energies, MDPI, vol. 14(9), pages 1-18, April.
  7. Paul Handro & Bogdan Dima, 2024. "Analyzing Financial Markets Efficiency: Insights from a Bibliometric and Content Review," Journal of Financial Studies, Institute of Financial Studies, vol. 16(9), pages 119-175, May.
  8. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
  9. Carlos A. Reyes Pérez & Miguel E. Iglesias Martínez & Jose Guerra-Carmenate & Humberto Michinel Álvarez & Eduardo Balvis & Fernando Giménez Palomares & Pedro Fernández de Córdoba, 2023. "Indoor Air Quality Analysis Using Recurrent Neural Networks: A Case Study of Environmental Variables," Mathematics, MDPI, vol. 11(24), pages 1-17, December.
  10. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
  11. Satya Verma & Satya Prakash Sahu & Tirath Prasad Sahu, 2024. "Two-Stage Hybrid Feature Selection Approach Using Levy’s Flight Based Chicken Swarm Optimization for Stock Market Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2193-2224, June.
  12. V. Lanzetta, 2024. "Transfer learning for financial data predictions: a systematic review," Papers 2409.17183, arXiv.org.
  13. Yanyan Cui & Lixin Liu, 2022. "Investor sentiment-aware prediction model for P2P lending indicators based on LSTM," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
  14. Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023. "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
  15. Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
  16. Supriya Bajpai, 2021. "Application of deep reinforcement learning for Indian stock trading automation," Papers 2106.16088, arXiv.org.
  17. Akash Doshi & Alexander Issa & Puneet Sachdeva & Sina Rafati & Somnath Rakshit, 2020. "Deep Stock Predictions," Papers 2006.04992, arXiv.org.
  18. Rui Zhang & Zhen Guo & Yujie Meng & Songwang Wang & Shaoqiong Li & Ran Niu & Yu Wang & Qing Guo & Yonghong Li, 2021. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China," IJERPH, MDPI, vol. 18(11), pages 1-14, June.
  19. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
  20. Kaushal Attaluri & Mukesh Tripathi & Srinithi Reddy & Shivendra, 2024. "News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models," Papers 2411.05788, arXiv.org.
  21. Javier Oliver Muncharaz, 2020. "Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks [Comparativa de los models clásicos de series temporales con la red neuronal recurrente ," Post-Print hal-03149342, HAL.
  22. Tashreef Muhammad & Tahsin Aziz & Mohammad Shafiul Alam, 2023. "Utilizing Technical Data to Discover Similar Companies in Dhaka Stock Exchange," Papers 2301.04455, arXiv.org.
  23. Nestoras Chalkidis & Rahul Savani, 2021. "Trading via Selective Classification," Papers 2110.14914, arXiv.org, revised Oct 2021.
  24. Gwiman Bak & Youngchul Bae, 2020. "Predicting the Amount of Electric Power Transaction Using Deep Learning Methods," Energies, MDPI, vol. 13(24), pages 1-30, December.
  25. Jun Zhang & Lan Li & Wei Chen, 2021. "Predicting Stock Price Using Two-Stage Machine Learning Techniques," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1237-1261, April.
  26. Mimansa Rana & Nanxiang Mao & Ming Ao & Xiaohui Wu & Poning Liang & Matloob Khushi, 2021. "Clustering and attention model based for intelligent trading," Papers 2107.06782, arXiv.org, revised Aug 2021.
  27. Lee, Donghyun & Kim, Mingyu & Lee, Beomhui & Chae, Sangwon & Kwon, Sungjun & Kang, Sungwon, 2022. "Integrated explainable deep learning prediction of harmful algal blooms," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
  28. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Yen-Lin Chen, 2022. "Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review," Mathematics, MDPI, vol. 10(8), pages 1-17, April.
  29. Singh, Sriramjee, 2020. "Predicting CBOT Corn Futures Prices by applying ML methods on Weather Data," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304595, Agricultural and Applied Economics Association.
  30. Andrew Brim & Nicholas S Flann, 2022. "Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-25, February.
  31. Shalini Sharma & Angshul Majumdar & Emilie Chouzenoux & Victor Elvira, 2023. "Deep State-Space Model for Predicting Cryptocurrency Price," Papers 2311.14731, arXiv.org.
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