Soft computing methods applied in forecasting of economic indices case study: forecasting of Greek unemployment rate using an artificial neural network with fuzzy inference system
Camelia Ioana Ucenic, Atsalakis S. George · International Conference on Mathematical and Computational Methods in Science and Engineering · 2008
The unemployment rate is an indicator used by investors to determine the health of the economy. It is also a measurement of the economy growth rate. Greece is a low-productivity economy. Statistics unfortunately cannot be totally taken at face value because earnings of many Greeks and immigrant workers are off-the-books. In addition, the immigrants make up nearly one-fifth of the work force, mainly in unskilled jobs. A 2003-2008 employment action plan included measures for the state to provide some 25,000 part-time jobs, more subsidies and tax incentives, greater social services (especially for women), training programs for the long-term unemployed, rent assistance and breaks unemployment rate to drop. The paper presents an ANFIS forecasting model. The results were presented and compared based on four different kinds of errors: MSE, RMSE, MAE and MAPE. The ANFIS model gives the best results for the case of six gauss membership functions and 250.000 epochs.