Name
Abstract | ||
|---|---|---|
Nowadays, artificial intelligence is frequently used to various fields including medicine, chemistry and forecasting. In this paper, artificial intelligence is applied to network traffic prediction. Due to that network traffic prediction plays an important role in network management, planning, traffic congestion control and traffic engineering. Seeking for more accurate network traffic prediction techniques, this paper proposed a new hybrid method (SPLSSVM) which based on seasonal adjustment (SA) and least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) to predict network traffic. The proposed method is examined by using the network traffic data from Lanzhou University. Empirical testing indicates that the proposed method can provide more accurate and effective results than the other forecasting methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/UIC-ATC-ScalCom.2014.100 | UIC/ATC/ScalCom |
Keywords | Field | DocType |
Network traffic prediction, Particle swarm optimization, Least square support vector machine | Particle swarm optimization,Traffic generation model,Data mining,Least squares support vector machine,Computer science,Multi-swarm optimization,Artificial intelligence,Network management,Traffic engineering,Network traffic simulation,Machine learning,Traffic congestion | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Yang | 1 | 277 | 61.06 |
| Yanhua Chen | 2 | 5 | 1.44 |
| Caihong Li | 3 | 7 | 4.01 |
| Xiangquan Gui | 4 | 0 | 0.34 |
| Lian Li | 5 | 189 | 40.80 |