Application of Chaos-support Vector Machine Regression in Traffic Prediction

A traffic forecasting model based on the support vector machine(SVM) and chaos was developed to improve the accuracy of the traffic prediction.Based on the phase space reconstruction,it calculates the real-time traffic's delay time,embedded dimension and Lyapunov exponent,and proves that the traffic chaos phenomena exists.That a chaos-SVM model was constructed and pairs of training samples was determined to forecast the real network traffic.The results show that the chaos-SVM model is able to predict network traffic effectively.In comparison with the BP neutral network,it has higher accuracy of prediction.