Combined Grey Forecast Model with Particle Swarm Optimizer

Prediction and monitoring of the telephone traffic concerned by the telecom operators to predict accurately and reliability of the results directly affect the effectiveness and long-term development of enterprises. Based on the time series type of telephone traffic data for its small term, and characteristics of the trend increasing, for this reason this paper use grey model GM(1,1). The telephone traffic itself has less volatility and a strong seasonal characteristics, this paper presents a new prediction model, combined grey forecast model. The model combined the longitudinal and horizontal data of the projected trend of the data to achieve short-term forecasting and seasonal prediction of binding targets, and thus improve the prediction accuracy. At the same time by the introduction of particle swarm optimizer(PSO) to improve the grey forecast model to compare with the Season Exponent and Grey Forecast method. This paper picked a province of China from 2004 to 2009 the monthly telephone traffic data, through different angles using grey model GM(1,1) to predict and compare the predicted results, the experiment can see the improved grey prediction model have more accurately predict the future telephone traffic.