A Radial Basis Function Neural Network-Based Fast Forecasting Model for Regional Economy

Abstract

The computational intelligence-based digital forecasting for social systems has been a novel tendency. This work takes forecasting of regional economy as the problem scenario, and introduces radial basis function neural network (RBFNN) to deal with this concern. Hence, an RBFNN-based fast forecasting model for regional economy is constructed in this paper. First, the economic flow data are encoded into sequential format, and RBFNN is employed to establish a sequential forecasting model that fits flow data. In addition, a fuzzy clustering method is further utilized to complete missing data in flow sequence, in order to improve the data quality for model calculation. In simulation procedure, the proposal is implemented on real economic operation data for assessment, and mean absolute error (MAE) is introduced to measure the forecasting performance. Several typical regression-based forecasting methods are introduced as the baseline. The experimental results show that MAE of the proposal is about 20%–30% lower than that of baseline methods, showing a better forecasting performance.