MATLAB Implementation of Wavelet Networks for Time Series Prediction

This source code implements a wavelet network using C++/VC++ programming, specifically designed for training and predicting time series data. The program constructs wavelet-based models to train on input time series data and can forecast future data trends. The implementation includes complete wavelet decomposition and reconstruction processes, along with corresponding training algorithms and prediction methods. Key technical components involve wavelet transform functions for multi-resolution analysis and neural network optimization techniques for model training. The code architecture features: - Wavelet decomposition functions implementing Mallat algorithm for time-frequency analysis - Neural network layers with backpropagation training for pattern recognition - Reconstruction modules that synthesize wavelet coefficients into predictions - Data preprocessing routines for time series normalization and windowing By studying this source code, developers can gain practical understanding of how wavelet networks combine signal processing techniques with neural networks for effective time series analysis and forecasting applications. The implementation demonstrates optimal parameter tuning for wavelet basis selection and network topology configuration.