Predicting Daily Stock Market Price using a Few-shot and Modified Transfer Learning
Keywords:
methods, performance, requirement, APR-FSLAbstract
Predicting the stock market and future stock prices is a difficult task. Stock customers have a strong requirement for market estimation. However, it frequently fails to produce successful results when predicting the stock price using a small amount of previous data. The Adaptive Deep ResNet with Few-Shot Learning deep model, which is new and built on transfer learning and Few-shot learning, is used in this research (APR-FSL). This work aims to maximize stock market price prediction performance, which presently offers predictions with the highest accuracy and lowest error rates. The proposed methodology focused on enhancing stock market prediction by fusing a Few-shot learning and knowledge transfer from transfer learning. Experiment results on the huge stock market dataset showed that the APR-FSL model outperforms other existing methods in terms of accuracy.
Downloads
References
Umer, M., Awais, M., & Muzammul, M. (2019). Stock market prediction using machine Learning (ML)Algorithms. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 97-116.
Krishna, V. ScienceDirect ScienceDirect NSE Stock Stock Market Market Prediction Prediction Using Using Deep-Learning Deep-Learning Models Models. Procedia Comput. Sci. 2018, 132, 1351–1362.
Rao, P. S., Srinivas, K., & Mohan, A. K. (2020). A survey on stock market prediction using machine learning techniques. In Lecture Notes in Electrical Engineering (pp. 923–931).
Kim, S. and Kang, M. (2019). Financial series prediction using Attention LSTM. arXiv preprint arXiv:1902.10877.
G. Ding, L. Qin, Study on the prediction of stock price based on the associated network model of lstm, International Journal of Machine Learning and Cybernetics (2019) 1–11.
Lien Minh, Dang et al. “Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network.” IEEE Access 6 (2018): 55392-55404.
Shah, A., Gor, M., Sagar, M. et al. A stock market trading framework based on deep learning architectures. Multimed Tools Appl 81, 14153–14171 (2022)
Wu, J.MT., Li, Z., Herencsar, N. et al. A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems (2021).
Wu, J.M.T., Li, Z., Srivastava, G., Tasi, M.H., Lin, J.C.W.: A graph-based convolutional neural network stock price prediction with leading indicators. Pract. Exp. Softw. (2020)
Nabipour, M., Nayyeri, P., Jabani, H., Shamshirband, S., & Mosavi, A. (2020). Deep learning for stock market prediction. https://doi.org/10.20944/preprints202003.0256.v1
Pang, X., Zhou, Y., Wang, P., Lin, W., & Chang, V. (2018). An innovative neural network approach for stock market prediction. The Journal of Supercomputing, 76(3), 2098-2118. https://doi.org/10.1007/s11227-017-2228-y
Kelotra, A., & Pandey, P. (2020). Stock market prediction using optimized deep-convlstm model. Big Data, 8(1), 5-24. https://doi.org/10.1089/big.2018.0143
Chung, H., Shin, Ks. Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Comput & Applic 32, 7897–7914 (2020). https://doi.org/10.1007/s00521-019-04236-3
Nti, I.K., Adekoya, A.F. & Weyori, B.A. A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. J Big Data 8, 17 (2021). https://doi.org/10.1186/s40537-020-00400-y
Nti, Isaac Kofi, Adekoya, Adebayo Felix and Weyori, Benjamin Asubam. "Efficient Stock-Market Prediction Using Ensemble Support Vector Machine" Open Computer Science, vol. 10, no. 1, 2020, pp. 153-163. https://doi.org/10.1515/comp-2020-0199.
(N.d.). Retrieved October 11, 2022, from Kaggle.com website: https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs).
Kang, W., Xiao, J., & Xue, J. (2022). Generative knowledge-based transfer learning for few-shot health condition estimation. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-022-00787-6.
K. M. He, X. Y. Zhang, S. Q. Ren, and J. veSun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, Las Vegas, NV, USA, June 2016.
Zhang, Y., Li, J., Wei, S., Zhou, F., & Li, D. (2021). Heartbeats classification using hybrid time-frequency analysis and transfer learning based on ResNet. IEEE Journal of Biomedical and Health Informatics, 25(11), 4175–4184. https://doi.org/10.1109/JBHI.2021.3085318.
H. Ouyang, X. Wei and Q. Wu, "Discovery and Prediction of Stock Index Pattern via Three-Stage Architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs," in IEEE Access, vol. 8, pp. 123683-123700, 2020, doi:10.1109/ACCESS.2020.3005994.
Zubair M, Fazal A, Fazal R, Kundi M. Development of stock market trend prediction system using multiple regression. Computational and mathematical organization theory. Berlin: Springer US; 2019. https://doi.org/10.1007/s1058 8-019-09292-7.
Vaishnavi Gururaj, Shriya, V. R., Ashwini, K. Stock Market Prediction using Linear Regression and Support Vector Machines. International Journal of Applied Engineering Research ISSN 0973-4562 Vol 14, no.8, (2019), pp. 1931-1934.
Deepika, N., & Nirupama Bhat, M. (2021). An efficient stock market prediction method based on Kalman filter. Journal of The Institution of Engineers (India) Series B, 102(4), 629–644. doi:10.1007/s40031-021-00583-9.
Dhabliya, D., Ugli, I.S.M., Murali, M.J., Abbas, A.H.R., Gulbahor, U. Computer Vision: Advances in Image and Video Analysis (2023) E3S Web of Conferences, 399, art. no. 04045,
Beemkumar, N., Gupta, S., Bhardwaj, S., Dhabliya, D., Rai, M., Pandey, J.K., Gupta, A. Activity recognition and IoT-based analysis using time series and CNN (2023) Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries, pp. 350-364.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.