Network traffic analysis and prediction based on APM

Traffic prediction is of significant importance for telecommunication network planning and network optimization. Since modeling and forecasting using traditional Box-Jenkins´ ARIMA is rather a complex process and time consuming, a novel approach called APM is studied and applied in this paper. APM is especially appropriate for time series exhibiting stable seasonal pattern and can be employed much simpler than ARIMA. Traffic series from a certain mobile network of Heilongjiang province in China is studied. Average daily traffic per month for the province as well as its every sub-region from July to December in 2009 is forecasted by using APM. The mean absolute percentage error (MAPE) for one-step ahead prediction is 2.11%, and MAPE for the 6 steps is smaller than 7%. The prediction result is of high precision and can be comparable with ARIMA.