Application of GA-SVM Time Series Prediction in Taz Forecasting
Forecasting the tax gross exactly is significant to carry on the macroscopic regulation efficiently under the market economy. Conventional linear macroscopic economic model is very difficult to hold non-linear phenomena in economic system, thus the tax forecasting error will increase. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, the application for tax forecasting is neglected. Based on regression arithmetic of SVM, support vector machine with genetic algorithm (GA-SVM) is proposed to forecast tax, in which genetic algorithm (GA) is used to determine the training parameters of support vector machine. The experimental results indicate that the proposed GA-SVM model can achieve great accuracy under the circumstance of small training data.
GA-SVM parameter optimization time series prediction taz forecasting
Sheng Lu Zhong-jian Cai Xiao-bin Zhang
School of Computer Science and Information Engineering Chongqing Technology and Business University Guangxi Special Equipment Supervision and Inspection Institute Nanning ,China
国际会议
北京
英文
1982-1984
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）