The HTS Network Load Prediction Based on ZTS-SVR Support Vector Machine Algorithm

摘要

The throughput of HTS is hundreds of times than traditional VSAT system and can provides broadband access for hundreds of thousands subscribers. However, the network load of the HTS is drastic fluctuations. The time series analysis algorithm is based on statistic model and obey some probability distribution which must satisfy hypothesis of stationary and independent identically distribution. Even if the non-stationary series can be converted to stationary series by serial difference, the detailed information will be lose during smoothing. This paper extends the support vector machine algorithm from discrete classification to continue time series, and presents a kernel function based on ANOVA decomposition algorithm. The simulation results show that the algorithm precision and rate of convergence is better than ARM1A model and SVR algorithm with classic RBF kernel.