Research on the Prediction Model of Chinese Tax Revenue Based on GM(1,1) and LSSVM

Abstract

Abstract: In view of the complex influencing factors of tax revenue, the highly non-linear relationship among the influencing factors and the difficulty in predicting tax revenue, this paper proposes to use GM (1, 1) Combined with LSSVM, and it calculates the tax forecasting of China. This paper selects the proportion of the first industry, the ratio of import and export trade to GDP, GDP, the number of urban employment population, the proportion of residents' disposable income and tax revenue in fiscal revenue as the influencing factors, and uses GM (1, 1) and LSSVM respectively to predict the tax revenue of our country, establishes the quadratic programming model to determine the optimal combination weight for the formation of the combination predicting model of tax revenue in our country, make an empirical analysis with the tax revenue of our country from 2000 to 2018 as the research object, and compare the prediction results with LSSVM model, GM (1,1) model and improved GM (1,1) model. The results show that the prediction model of China's tax revenue based on GM (1,1) and LSSVM has a high fitting accuracy with the test set, which can reflect the complex non-linear relationship between various factors. It is of great significance for the development of prediction on Chinese tax revenue and the formulation of a scientific and effective national financial budget.