A Combined Forecasting Model for Satellite Network Self-Similar Traffic

Abstract

Since satellite network traffic is self-similar and long-range-dependent (LRD), after analyzing current network traffic forecasting models, a satellite network traffic combined forecasting model that is based on the decomposition fruit fly optimization algorithm-extreme learning machine (FOA-ELM) is proposed. This forecasting model decomposes LRD network traffic into multiple short-range dependent (SRD) components via empirical mode decomposition (EMD), applies the FOA-ELM forecasting model to the decomposed high-frequency components, and applies the ELM forecasting model to low-frequency components. The simulation results show that the forecasting model can improve the forecasting accuracy and forecasting speed, reduce the complexity, and achieve effective and efficient forecasting of satellite network traffic.