Deep Learning Model for Fusing Spatial and Temporal Data for Stock Market Prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s10614-023-10464-6.pdf
Reference47 articles.
1. Aithal, P. K., Acharya, D., & Geetha, M. (2019). Identifying significant macroeconomic indicators for Indian stock markets. IEEE Access, 7, 143829–143840. https://doi.org/10.1109/ACCESS.2019.2945603
2. Aldhyani, T., & Alzahrani, A. (2022). Framework for predicting and modeling stock market prices based on deep learning algorithms. MDPI, 11, 1–19. https://doi.org/10.3390/electronics11193149
3. Alsubaie, Y., Hindi, K. E., & Alsalman, H. (2019). Cost-sensitive prediction of stock price direction: Selection of technical indicators. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2945907
4. Arvind, S., & Pradeep, S. (2022). Uncertainty optimization based feature selection model for stock marketing. Computational Economics. https://doi.org/10.1007/s10614-022-10344-5
5. Bansal, M., Goyal, A., & Choudhary, A. (2022). Stock market prediction with high accuracy using machine learning techniques. In 4th international conference on innovative data communication technologies and application (Vol. 215, pp. 247–265). Elsevier. https://doi.org/10.1016/j.procs.2022.12.028
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1. Correction to: Deep Learning Model for Fusing Spatial and Temporal Data for Stock Market Prediction;Computational Economics;2023-12-29
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