Machine Learning-Based Comprehensive Framework for Stock Prediction

This study explores LSTM networks for stock price prediction, leveraging their ability to capture temporal dependencies. It incorporates historical prices, technical indicators, and news sentiment for accuracy. This study preprocessed the data, trained the LSTM model, compared it to linear methods using real economic datasets, and applied metrics such as MAE and MSE. This work contributes to improve the effectiveness of deep learning in financial forecasting and proves more valuable to investors and analysts. LSTM networks are good at forecasting complex temporal variations that are important in market size forecasting. Through preprocessing and feature extraction, the LSTM model captures a series of economic time series. This study focuses on hyperparameter optimization and external parameters, which significantly improve the accuracy of the model.