Telecommunication Traffic Prediction Based on Improved LSSVM

Telecommunication traffic prediction is an important aspect of data analysis and processing in communication networks. In this study, we utilize the least-squares support vector machine (LSSVM) prediction method to improve the prediction performance of telecommunication traffic. As the parameters of LSSVM are difficult to determine, we propose to optimize the LSSVM parameters using the improved artificial bee colony (IABC) algorithm based on the fitness-prediction strategy (i.e. FP-IABC). We employ real traffic data collected on site to establish a telecommunication traffic forecasting model based on FP-IABC optimizing LSSVM (FP-IABC-LSSVM). The experiment results indicate that in the case involving no increase in the computational complexity, the proposed telecommunication traffic forecasting model-based FP-IABC-LSSVM has a higher prediction accuracy than the prediction model based on the ABC optimizing LSSVM (ABC-LSSVM), particle swarm optimizing LSSVM (PSO-LSSVM), and genetic algorithm optimizing LSSVM (GA-LSSVM). Further, with respect to the standard root mean square error and the average computation time, the proposed FP-IABC-LSSVM is the optimal prediction method of all of the comparison methods. The proposed prediction method not only improves the prediction accuracy, but also reduces the average computation time.