Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.97 | ICNC (1) |
Keywords | Field | DocType |
simulated annealing-support vector regression machine,mobile communication,mobile operator,mobile traffic forecasting,regression analysis,tariff,curve fitting,remaining seasonal part,general trend,sasvr,mobile traffic,general trend part,seasonal part,telecommunication computing,telecommunication traffic,traffic forecasting,market trend,simulated annealing,tariff level,improved method,support vector machines,support vector regression,seasonality,predictive models,computational modeling,control network,forecasting,accuracy | Simulated annealing,Mathematical optimization,GRASP,Curve fitting,Computer science,Regression analysis,Support vector machine,Tariff,Artificial intelligence,Operator (computer programming),Machine learning,Mobile telephony | Conference |
Volume | ISBN | Citations |
1 | 978-0-7695-3736-8 | 1 |
PageRank | References | Authors |
0.35 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanfeng Tan | 1 | 1 | 0.35 |
| Xizhong Qin | 2 | 5 | 2.88 |
| Zhenhong Jia | 3 | 29 | 15.13 |
| Chun Chang | 4 | 1 | 0.35 |
| Hao Wang | 5 | 1 | 0.35 |