Short-Term Traffic Flow Prediction Based on Wavelet Neural Network

MATLAB implementation of short-term traffic flow prediction using wavelet neural network principles, featuring algorithm explanation and key function descriptions

Time Series Prediction Using Wavelet Neural Networks – Short-Term Traffic Flow Forecasting

Time series data represents sequences that change randomly over time, where forecasting involves using historical data points to predict current and future values. Traditional time series prediction methods in stochastic process theory typically employ linear models such as AR, MA, and ARMA models. However, these models require manual selection of model types and determination of orders, often resulting in significant prediction errors. Wavelet theory, an emerging mathematical method, has gained prominence in recent years. Wavelet neural networks combine wavelet analysis with neural networks to effectively address time series prediction challenges. This case study demonstrates the application of wavelet neural networks for traffic flow prediction, highlighting their effectiveness in time series forecasting through practical implementation and algorithm validation.