Using autoregressive integrated moving average models in the analysis and forecasting of mobile network traffic data

Abstract

Developing prediction models for mobile networks have been increasing in recent years, in response to the ever increasing volumes of customer traffic and also to understand the characteristics of traffic pattern. This study seeks to evaluate the forecasting performance of Autoregressive Integrated Moving Average (ARIMA) models by using an empirical data measured from a live High Speed Downlink Packet Access (HSDPA) telecommunication network operator with coverage in the northern part of Ghana. To determine the best ARIMA model, a number of statistical analysis and tests were carried out. The models with the minimum information criteria values were selected: ARIMA (2,1,3), ARIMA (0,1,2) and ARIMA (1,1,1). Comparing the actual and predicted traffic data show that ARIMA (0,1,2) is the best model.