MATLAB RBF Neural Network Implementation for Stock Market Prediction

This MATLAB RBF neural network implementation specializes in stock market prediction applications. The code features comprehensive functionality for generating error analysis plots and prediction visualization charts, enabling users to conduct in-depth analysis and forecasting of stock market trends. The implementation utilizes radial basis function networks with optimized spread parameters and center selection algorithms to enhance prediction accuracy.

Key technical components include: - Data preprocessing modules for stock price normalization and feature extraction - RBF network training using orthogonal least squares learning algorithm - Dynamic error calculation and visualization through matplotlib integration - Prediction output generation with confidence interval estimation - Customizable network architecture parameters (hidden layer neurons, spread factors)

Users can leverage this implementation to gain precise insights into market fluctuations and make data-driven investment decisions. The reliability and accuracy have been validated through backtesting with historical stock data, making it an essential tool for quantitative analysts and investment professionals. The code includes detailed comments explaining the mathematical foundation of RBF networks and implementation best practices for financial time series forecasting.