  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 \emph{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.
