Stock Market Prediction Using Machine Learning: A Comprehensive Review on Long Short-Term Memory Techniques

Computer Engineering

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.

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