Time Series Prediction Using Wavelet Neural Networks

One approach for time series prediction involves using Wavelet Neural Networks (WNN), which combine wavelet transform analysis with artificial neural networks. This prediction model first decomposes the time series signal into different frequency bands at various scales through wavelet transformation. These frequency components then serve as inputs to a neural network that undergoes training and learning processes to generate future time series predictions. In MATLAB implementation, key steps include: - Using wavelet decomposition functions (e.g., wavedec) to break down the time series into approximation and detail coefficients - Selecting appropriate wavelet families (Daubechies, Symlets, etc.) and decomposition levels based on signal characteristics - Designing neural network architecture with input nodes corresponding to wavelet coefficients, hidden layers for feature learning, and output nodes for predictions - Implementing training algorithms (e.g., backpropagation, Levenberg-Marquardt) to optimize network weights - Validating model performance using metrics like RMSE and MAPE on test datasets The MATLAB program typically involves wavelet toolbox functions for signal processing and neural network toolbox functions for model building, creating an integrated framework for accurate time series forecasting.