Unemployment rate prediction has become critically significant, because it can help government to make decision and design policies. In previous studies, traditional univariate time series models and econometric methods for unemployment rate prediction have attracted much attention from governments, organizations, research institutes, and scholars. Recently, novel methods using search engine query data were proposed to forecast unemployment rate. In this paper, a data mining framework using search engine query data for unemployment rate prediction is presented. Under the framework, a set of data mining tools including neural networks (NNs) and support vector regressions (SVRs) is developed to forecast unemployment trend. In the proposed method, search engine query data related to employment activities is firstly extracted. Secondly, feature selection model is suggested to reduce the dimension of the query data. Thirdly, various NNs and SVRs are employed to model the relationship between unemployment rate data and query data, and genetic algorithm is used to optimize the parameters and refine the features simultaneously. Fourthly, an appropriate data mining method is selected as the selective predictor by using the cross-validation method. Finally, the selective predictor with the best feature subset and proper parameters is used to forecast unemployment trend. The empirical results show that the proposed framework clearly outperforms the traditional forecasting approaches, and support vector regression with radical basis function (RBF) kernel is dominant for the unemployment rate prediction. These findings imply that the data mining framework is efficient for unemployment rate prediction, and it can strengthen government’s quick responses and service capability.
Unemployment rate prediction Data mining Search engine query data Government service