Tax forecasting theory and model based on SVM optimized by PSO

摘要

The construction of tax forecasting model is difficult due to its uncertain, non-linear, dynamic and complicated characteristics. It is difficult to describe the non-linear characteristics of tax forecasting by traditional methods. In the study, the novel forecasting method based on the combination of support vector machine (SVM) and particle swarm optimization (PSO) is proposed to the tax forecasting. The non-linear relationship in tax forecasting is efficiently represented by support vector machine, and particle swarm optimization is used to select the training parameters of support vector machine. The tax forecasting model is constructed by support vector machine optimized by particle swarm optimization (PSVM) on the basis of research for the proposed forecasting model. The tax forecasting cases are used to testify the forecasting performance of the proposed model. The experimental results demonstrate that the proposed PSVM model has good forecasting performance.