Year: 2025
Type: article
Abstract: This study introduces a MATLAB-based framework for stock market analysis and forecasting utilizing historical price data.The system allows users to select any stock and specify a custom date range for analysis.Two predictive models are employed: ARIMA for short-term linear trend estimation and LSTM for deep learningbased forecasting.The framework encompasses data preprocessing, train-test splitting, next-day price prediction, and result visualization.By combining statistical and neural network approaches, this tool aims to facilitate informed decision-making in stock trading and investment strategies.The integration of these methods provides a comprehensive approach to stock market analysis, potentially enhancing the accuracy and reliability of price predictions.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412046034