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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Stock Market Prediction Using Machine Learning: A Comprehensive Review with Emphasis on Long Short-Term Memory Techniques |
| Authors Name: | Chaitali Prakash Bodke , Dr. Varsha Patil , Dr. Ranjit Gawande |
| Unique Id: | IJSDR2404177 |
| Published In: | Volume 9 Issue 4, April-2024 |
| Abstract: | Predicting stock market prices accurately is a challenging task due to the complex and dynamic nature of financial markets. Traditional methods often fall short in capturing the intricate patterns and interrelationships present in stock market data. In recent years, machine learning techniques have emerged as powerful tools for stock market prediction. In this study, we focus on using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), for stock market prediction. LSTM networks have shown promising results in capturing temporal dependencies and patterns in sequential data, making them well-suited for modeling stock market data which exhibits time-series characteristics. By leveraging historical stock price data along with other relevant features, LSTM networks can learn to predict future stock prices with reasonable accuracy. We propose a solution that utilizes LSTM techniques to predict stock market prices in real-time. Our approach involves preprocessing corporate stock data, training LSTM models on historical data, and generating predictions for future stock prices. The predicted prices are presented graphically, providing a visual representation of the expected price movements. The effectiveness of our proposed approach is demonstrated through a comprehensive evaluation using real-world stock market data. We compare our results with existing methods and showcase the advantages of using LSTM networks for stock market prediction. |
| Keywords: | Index Terms— stock market, price prediction, LSTM, machine learning, recurrent neural networks. |
| Cite Article: | "Stock Market Prediction Using Machine Learning: A Comprehensive Review with Emphasis on Long Short-Term Memory Techniques", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.1222 - 1227, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404177.pdf |
| Downloads: | 000338173 |
| Publication Details: | Published Paper ID: IJSDR2404177 Registration ID:211002 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 1222 - 1227 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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