A Block Regression Model for Short-Term Mobile Traffic Forecasting

TL;DR

This paper introduces a low-complexity Block Regression model for short-term mobile traffic forecasting that leverages traffic characteristics like periodicity and spatial similarity, achieving accuracy comparable to existing models.

Contribution

The paper proposes a novel Block Regression model utilizing seasonal differentiation, reducing complexity by using a single model for all base stations while maintaining accuracy.

Findings

BR model achieves similar accuracy to existing models.

BR model has significantly lower complexity.

Model effectively captures periodicity and spatial similarity.

Abstract

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.