A novel neural network model using Box-Jenkins technique and response surface methodology to predict unemployment rate

The study presents a novel semiparametric prediction system for the Taiwan unemployment rate series. The prediction method incorporated into the system consists of a neural network model that estimates the trend, as well as a Box-Jenkins prediction of the residual series. The response surface methodology is employed to find the appropriate setup of network parameters as the neural network is applied. Also, extensive studies are performed on the robustness of the built network model using different specified censoring strategies. In terms of the adaptability of the Box-Jenkins method, the prediction intervals of the system can be successfully constructed. To demonstrate the effectiveness of our proposed method, the monthly unemployment rate from June 1983 to February 1992 is evaluated using a neural network model with Box-Jenkins technique and other alternative methods, e.g. space-time series analysis, univariate ARIMA model and state space model. Analysis results demonstrate that the proposed method outperforms other statistical methodologies.