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Application of Support Vector Machine to Mobile Communications in Telephone Traffic Load of Monthly Busy Hour Prediction

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5 Author(s)
Rui Han ; Coll. of Inf. Sci. & Eng., Xinjiang Univ., Urumqi, China ; Zhenhong Jia ; Xizhong Qin ; Chun Chang
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Telephone traffic of busy hour is one of indicators of load capacity of telecommunication network, which has a significant meaning to dilate and modify the network. A good performance of predicting the monthly busy hour traffic load is cared about by the mobile operators. As a promising learning theory, support vector machine (SVM) has been studied and applied in a wide area, such as financial markets and weather forecast. In this paper, we use SVM to forecast monthly busy hour traffic load of two regions in Xinjiang. A good result has been achieved via an improved grid search method for the search of hyper-parameter of SVM.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:3 )

Date of Conference:

14-16 Aug. 2009

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