Forecasting of Busy Telephone Traffic Based on Wavelet Transform and ARIMA-LSSVM

Abstract

In order to improve the prediction accuracy of busy telephone traffic which is influenced by multiple factors, this paper proposes a combined forecasting model which takes the influence of multiple factors into consideration and combines three models ——wavelet transform, autoregressive integrated moving average (ARIMA) model and least squares support vector machines (LSSVM) model, LSSVM is optimized by particle swarm optimization (PSO). Correlation analysis is firstly applied to the busy telephone traffic data to obtain the key factors which influence the busy telephone traffic. Then wavelet transform is used to decompose and reconstruct the telephone traffic data to get the low-frequency and high-frequency components. The low-frequency component is loaded into ARIMA model to predict, while the high-frequency component and the obtained key factors are loaded into PSO-LSSVM model to predict. Finally the forecasting result is achieved by the superposition of predictive values. The simulation results show that the proposed model has higher prediction accuracy and strong generalization ability.