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Original Article
Stock Market Prediction Using Deep Learning and Stream lit
Arshiya Tabassum1
Dr. Mohd Rafi Ahmed2
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 01-06
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250605001
References
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16. S. Selvin, R. Vinayakumar, E. Gopalakrishnan, V. Menon, and K. Soman, “Stock Price Prediction Using LSTM, RNN and CNN-Sliding Window Model,” in Proc. Int. Conf. Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 1643–1647.
17. R. Krauss, T. Do, and N. Huck, “Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500,” European Journal of Operational Research, vol. 259, no. 2, pp. 689–702, 2017.
18. J. Shynkevich, T. McGinnity, S. Coleman, and A. Belatreche, “Stock Price Prediction Based on Stock-Specific and Sub-Industry-Specific News Articles,” IEEE Trans. Neural Networks and Learning Systems, vol. 27, no. 3, pp. 590–604, 2016.
19. Yahoo Finance, “Historical Market Data,” [Online]. Available: https://finance.yahoo.com. [Accessed: 20-Aug-2025].
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23. Z. Zhang, “Stock Market Prediction via Multi-Source Multiple Instance Learning,” Pattern Recognition, vol. 103, pp. 107–117, 2020.
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25. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Time-Series Data Representation,” in Proc. IEEE CVPR Workshop on Deep Learning for Time Series, 2015, pp. 3431–3440.
2. R. Pascanu, T. Mikolov, and Y. Bengio, “On the Difficulty of Training Recurrent Neural Networks,” in Proc. Int. Conf. Machine Learning (ICML), 2013, pp. 1310–1318.
3. J. Brownlee, Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery, 2018.
4. Y. Bengio, I. Goodfellow, and A. Courville, Deep Learning, MIT Press, 2016.
5. M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proc. USENIX OSDI, 2016, pp. 265–283.
6. F. Chollet, “Keras,” [Online]. Available: https://keras.io. [Accessed: 20-Aug-2025].
7. Streamlit Inc., “Streamlit — Turn Data Scripts into Shareable Web Apps in Minutes,” [Online]. Available: https://streamlit.io. [Accessed: 20-Aug-2025].
8. T. Fischer and C. Krauss, “Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions,” European Journal of Operational Research, vol. 270, no. 2, pp. 654–669, 2018.
9. A. Nelson, M. Pereira, and R. de Oliveira, “Stock Market’s Price Movement Prediction with LSTM Neural Networks,” in Proc. Int. Joint Conf. Neural Networks (IJCNN), 2017, pp. 1419–1426.
10. Y. Chen, Y. Zhu, and Y. Chen, “Stock Market Prediction Using Machine Learning Algorithms,” CS229 Project Report, Stanford University, 2015.
11. G. Atsalakis and K. Valavanis, “Surveying Stock Market Forecasting Techniques – Part II: Soft Computing Methods,” Expert Systems with Applications, vol. 36, no. 3, pp. 5932–5941, 2009.
12. H. Bao, J. Yue, and Y. Rao, “A Deep Learning Framework for Financial Time Series Using Stacked Autoencoders and Long-Short Term Memory,” PLOS ONE, vol. 12, no. 7, pp. 1–24, 2017.
13. K. Patel, S. Patel, and P. Patel, “Predicting Stock and Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques,” Expert Systems with Applications, vol. 42, no. 1, pp. 259–268, 2015.
14. J. Bollen, H. Mao, and X. Zeng, “Twitter Mood Predicts the Stock Market,” Journal of Computational Science, vol. 2, no. 1, pp. 1–8, 2011.
15. R. Guresen, G. Kayakutlu, and T. U. Daim, “Using Artificial Neural Network Models in Stock Market Index Prediction,” Expert Systems with Applications, vol. 38, no. 8, pp. 10389–10397, 2011.
16. S. Selvin, R. Vinayakumar, E. Gopalakrishnan, V. Menon, and K. Soman, “Stock Price Prediction Using LSTM, RNN and CNN-Sliding Window Model,” in Proc. Int. Conf. Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 1643–1647.
17. R. Krauss, T. Do, and N. Huck, “Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500,” European Journal of Operational Research, vol. 259, no. 2, pp. 689–702, 2017.
18. J. Shynkevich, T. McGinnity, S. Coleman, and A. Belatreche, “Stock Price Prediction Based on Stock-Specific and Sub-Industry-Specific News Articles,” IEEE Trans. Neural Networks and Learning Systems, vol. 27, no. 3, pp. 590–604, 2016.
19. Yahoo Finance, “Historical Market Data,” [Online]. Available: https://finance.yahoo.com. [Accessed: 20-Aug-2025].
20. Kaggle Inc., “Stock Market Datasets,” [Online]. Available: https://www.kaggle.com. [Accessed: 20-Aug-2025].
21. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014.
22. A. Graves, “Supervised Sequence Labelling with Recurrent Neural Networks,” Studies in Computational Intelligence, vol. 385, Springer, 2012.
23. Z. Zhang, “Stock Market Prediction via Multi-Source Multiple Instance Learning,” Pattern Recognition, vol. 103, pp. 107–117, 2020.
24. D. Shen and H.-I. Suk, “Deep Learning in Financial Time-Series Analysis: A Survey,” IEEE Access, vol. 8, pp. 209310–209326, 2020.
25. J. Long, E. Shelhamer, and T. Darrell, “Fully Convolutional Networks for Time-Series Data Representation,” in Proc. IEEE CVPR Workshop on Deep Learning for Time Series, 2015, pp. 3431–3440.
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