SOURCE IEEE/CIC International Conference on Communications in China (ICCC 2015), Shenzhen, China, Nov.2-4，2015
Accurate mobile traffic forecast is important for
efficient network planning and operations. However, existing
traffic forecasting models have high complexity, making the
forecasting process slow and costly. In this paper, we analyze
some characteristics of mobile traffic such as periodicity, spatial
similarity and short term relativity. Based on these characteristics,
we propose a Block Regression (BR) model for mobile traffic
forecasting. This model employs seasonal differentiation so as to
take into account of the temporally repetitive nature of mobile
traffic. One of the key features of our BR model lies in its low
complexity since it constructs a single model for all base stations.
We evaluate the accuracy of BR model based on real traffic data
and compare it with the existing models. Results show that our
BR model offers equal accuracy to the existing models but has
much less complexity.