Network Traffic Prediction and Result Analysis Based on Seasonal ARIMA and Correlation Coefficient

Abstract

Traffic prediction is of significant importance for telecommunication network planning and network optimization. In this paper, the traffic series from a certain mobile network in Heilongjiang province in China is studied. The characteristics in respect of both trend and periodicity are explored with autocorrelation function. Based on the characteristics exhibited in the traffic series, multiplicative seasonal autoregressive integrated moving average model (ARIMA) is employed to make traffic series prediction. Average daily traffic per month for the province as well as its every sub-region from July to December in 2009 is forecasted and compared with the actual operation data. The mean absolute percentage error (MAPE) for one-step ahead prediction is 1.382%, and MAPE for the 6 steps is within 6%. The prediction result is of high precision. Furthermore, the cause for the big prediction error in 2 regions is analyzed, and the appropriateness of the model is testified on the opposite aspect. This paper also provides an effective method by using correlation coefficients to analyze the cause for significant prediction errors which do not happen in time series prediction applications rarely.