Particle swarm optimized partial least square support vector regression model for tax revenue prediction

Wu Ping

Abstract

Chinese tax revenue is non-linear and coupled, and is influenced by many factors. Therefore, traditional forecasting methods are not sufficient to predict the value of it. In this paper, disadvantages of the existing forecasting methods are analyzed. Then partial least square support vector machine (PLS-SVR) is used to construct a tax revenue prediction model. An improved particle swarm algorithm is used to optimize the parameter set of (C, σ2), which influences the performance of this model directly. By doing so, this model can deal with the nonlinearity and multi-factors of tax revenue, and ensure stability and accuracy of support vector machine based regression. Case study on Chinese tax revenue during the last 30 years demonstrates that the optimized PLS-SVR model is much more accurate than other prediction methods.