Comparative Analysis of Optimization Techniques in Ensemble Models for Stock Price Prediction

Stock price prediction is a pivotal task in fiscal requests, abetting investors make educated judgments. This research project proposes an ensemble model that combines sentiment analysis with the sliding window system for enhanced forecast accuracy. The paper involves extracting textual features similar as polarity and subjectivity through sentiment analysis and deriving historical data features using the sliding window method. These features are also integrated into a single dataset for model input. Three Recurrent Neural Network (RNN) models, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN), are trained using this dataset, and their performance criteria, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-Squared (R
²
), are estimated. Also, ensemble models are constructed using Particle Swarm Optimization (PSO) and the Differential Evolution (DE) system for integrating the labors of different models. The finding advances the understanding on stock price forecasting techniques and offer insights into opting optimal models for real-world applications.