Predicting Stock Prices with Advanced Deep Learning Technique: An LSTM-Based Approach

This chaptere explores the evolution of stock price prediction, transitioning from traditional statistical methods to advanced deep learning techniques, with a focus on Long Short-Term Memory (LSTM) networks. It highlights the challenges in capturing complex nonlinear relationships in financial data and the limitations of traditional models. The paper proposes an LSTM-based approach, detailing the data collection, preprocessing, and feature extraction processes. It describes the architecture and functions of LSTM, including forget gates, input gates, and output gates, and explains how LSTM addresses the vanishing gradient issue. The methodology involves training an LSTM model with data from Apple, Tesla, Google, and Netflix shares, using a sliding window segmentation approach. The results demonstrate the model's accuracy, with an average R2 score of 99% across all datasets. Comparative analyses with Linear Regression and ARIMA models underscore the superiority of LSTM in predicting stock prices. The paper concludes by discussing the implications for investors and analysts, suggesting further enhancements such as integrating sentiment analysis and performing comparative analyses with conventional time series forecasting methods.