International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The prediction of stock prices is a complex task due to the influence of numerous volatile and non-linear factors. While traditional machine learning algorithms like Extreme Gradient Boosting (XGBoost) are powerful tools for structured data, they often struggle to inherently capture the temporal dependencies in financial time series. This study investigates the application of a Long Short-Term Memory (LSTM) network, a deep learning architecture designed for sequential data, for predicting stock prices. We conduct a comparative analysis, benchmarking the LSTM's performance against a strong XGBoost model on historical data of [e.g., Apple Inc. (AAPL)]. The methodology involves meticulous data preprocessing, feature engineering for XGBoost, and sequence modeling for LSTM. Results demonstrate that the LSTM model significantly outperforms the XGBoost benchmark, achieving a lower Mean Absolute Percentage Error (MAPE) of [LSTM MAPE]% compared to [XGBoost MAPE]%. This finding underscores the strength of models like LSTM and XGBoost in automatically learning temporal patterns and long-term dependencies without the need for extensive manual feature engineering.

Keywords: Stock Price Prediction, LSTM, XGBoost, Time Series Forecasting, Deep Learning, Machine Learning, Comparative Analysis.

Downloads: | DOI: 10.17148/IJARCCE.2025.141214