A Novel Approach to predict the Stock Price using LSTM and Linear Regression

Authors

  • Preetjot Kaur  Department of CSE, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India
  • Karan Marwaha  Department of CSE, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India
  • Keshav Kumar  Department of CSE, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India

DOI:

https://doi.org/10.32628/IJSRSET2411111

Keywords:

Stock Price Prediction, Machine Learning, Linear Regression, long short-term memory

Abstract

Stock price prediction is a challenging and crucial task in financial markets. Traditional methods often struggle to capture the complex patterns present in stock price movements. In this study, we propose a hybrid model combining Long Short-Term Memory (LSTM) and Linear Regression techniques to improve the accuracy and robustness of stock price predictions. We evaluate the performance of our hybrid model using historical stock price data and compare it with individual LSTM and linear regression models. The experiments demonstrate that the hybrid model outperforms the standalone models in terms of accuracy and robustness.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2024-02-29

Issue

Volume 11, Issue 1, January-February-2024

Section

Research Articles

License

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Preetjot Kaur, Karan Marwaha, Keshav Kumar "A Novel Approach to predict the Stock Price using LSTM and Linear Regression" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 11, Issue 1, pp.118-123, January-February-2024. Available at doi : https://doi.org/10.32628/IJSRSET2411111