Internet Traffic Forecasting Model using Self Organizing Map and Support Vector Regression Method

Abstract

Internet traffic forecasting is one of important aspect in order to fulfill the customer demand. So, the service quality of internet service provider (ISP) can be maintained at the good level. In this study self organizing map (SOM) and support vector regression (SVR) algorithm are used as forecasting method. SOM is first used to decompose the whole historical data of traffic internet into clusters, while SVR is used to build a forecasting model in each cluster. This method is used to forecast ISPs traffic internet in Jakarta and surrounding areas. The result of this study shows that SOM-SVR method gives more accurate result with smaller error value compared to that of the SVR method.