An improved method of traffic forecasting based on tariff-SASVR

Authors:
Yanfeng Tan;Xizhong Qin;Zhenhong Jia;Chun Chang;Hao Wang
Affiliations:
College of Information Science & Engineering, Xinjiang University, Urumqi, Xinjiang, P.R.China;College of Information Science & Engineering, Xinjiang University, Urumqi, Xinjiang, P.R.China;College of Information Science & Engineering, Xinjiang University, Urumqi, Xinjiang, P.R.China;Xinjiang Mobile Communication Company, Urumqi, Xinjiang, P.R.China;Xinjiang Mobile Communication Company, Urumqi, Xinjiang, P.R.China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

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Abstract

Traffic forecasting is critical for mobile operators to grasp market trends and control network capacity. Therefore, an improved method of forecasting for mobile traffic is presented in this paper. The traffic is divided into the general trend part and seasonal part to forecast them respectively. The general trend is predicted by fitting the curve of general trend on tariff level; and the remaining seasonal part is predicted by simulated annealing-support vector regression machine (SASVR) which uses simulated annealing (SA) to select the super-parameters of SVR. The experimental results show that not only this method improves the prediction accuracy but it provides mobile operators with a visual expression of the relationship between traffic and the tariff level.