Neural Network for Rolling Prediction of Stock Single Indicator

This document provides MATLAB source code for implementing a neural network designed for rolling prediction of single stock indicators. The code architecture utilizes an ELMAN neural network structure, which features recurrent connections that allow for dynamic temporal processing - making it suitable for time-series forecasting applications. You can modify and adapt this implementation to address your specific regression prediction problems by adjusting network parameters, input features, and prediction horizons. Key implementation aspects include: - Time-series data preprocessing and normalization - Rolling window mechanism for sequential prediction - ELMAN network configuration with hidden layer recurrence - Backpropagation through time training algorithm Please note this code is intended solely for research purposes to demonstrate general ELMAN neural network applications. It is NOT suitable for real-world investment analysis or decision-making due to potentially significant prediction errors and inherent limitations in financial forecasting. Exercise extreme caution and do not use for actual trading activities.