The Unemployment Rate Forecast Model Based on Neural Network

Unemployment is a critical problem in China. It is also a major factor relative with the economic development of a society. Hence, unemployment forecast model as an important way of studying economy and society should be paid more attention. An accurate predicted outcome is significant for the government to set the relative policies. In the paper, there are some influencing indicators suggested by experts. We use back-propagation neural network (BPNN) and Elman neural network to predict unemployment rate respectively, and do a comparison between them. However, before inputting the data into the neural network to calculate, there is some preprocessing should do to accelerate the neural network´s computing speed, here, principal component analysis (PCA) is adopted. PCA is a kind of statistics analysis tool for condensing the results.