MATLAB RBF Neural Network File for Stock Market Prediction with Error and Forecast Visualization

This MATLAB RBF neural network implementation is particularly suitable for stock market prediction applications. The code includes comprehensive functionality for generating detailed error analysis plots and forecast visualization charts, enabling users to conduct thorough market trend analysis and stock price movement predictions. Through this implementation, users can input relevant financial data and obtain accurate forecasting results, supporting informed investment decision-making. The implementation utilizes Radial Basis Function neural networks with Gaussian activation functions and includes data normalization preprocessing, center selection algorithms (like k-means clustering), and width parameter optimization. The visualization module employs MATLAB's plotting functions to display training error convergence, prediction vs actual comparisons, and confidence intervals. Key functions include: - Data preprocessing and normalization routines - RBF network training with gradient descent or pseudo-inverse methods - Prediction accuracy evaluation metrics (MSE, RMSE) - Interactive plotting capabilities for result visualization This MATLAB RBF neural network package serves as a robust and practical tool for investors seeking to enhance their stock market performance through advanced machine learning techniques. The code structure allows for easy parameter adjustment and integration with real-time data feeds.