The Forecasting Based On Support Vector Machine And Its Application In The Empirical Analysis Of China's Urban Unemployment Rate

Unemployment has always been a major social and economic development issue throughout the world. It is both a comprehensive economic problem, but also a complex social problem. Meanwhile, unemployment is one of three important indicators in microeconomic. Therefore, the study of urban unemployment rate has a positive and practical significance.Daters of the thesis which come from Bureau of Statistics People's Republic of China, National statistical Database, Development Research Centre of the State Council Information Networks and Hexun Networks predict China's urban unemployment rate using regression analysis and support vector regression methods.ChapterⅠintroduces the background and significance of the paper. It also does some research in unemployment rate and Support Vector Machines. ChapterⅡpresents the relevant forecasting methods, which are studied from the qualitative and quantitative prediction. The quantitative prediction contents Time Series prediction, Multiple Regression forecast, Grey Prediction, Artificial Neural Networks prediction, Support vector Regression and compound prediction. ChapterⅢdescribes the core of the support vector machines theory in three aspects, which shows from machine learning, statistical learning theory and support vector machines. Support vector machines method is based on an algorithm of statistical learning theory. The basic theory is VC dimension (Vapnik and Chervonenkis dimension) and structural risk minimization principle. ChapterⅣhas made an empirical analysis in China's urban unemployment in the way of multiple linear regression, nonlinear regression, neural networks, support vector machine of linear kernel and Gaussian radial basis function. These five methods compares from three aspects,including the curve fitting, relative error and the fitting precision..Therefore,we come to a conclusion that the SVM regression method is an ideal curve fitting. Support vector regression selects linear kernel function and Gaussian Radial Basis Function kernel for its fitting. The fitting accuracy is of 0.177% and 0.195%. Finally, this paper forecasts China's urban unemployment rate from 2010 to 2015 using those above five methods and obtained its results.