Forecasting value of agricultural imports using a novel two-stage hybrid model

Agricultural imports are becoming increasingly important in terms of their impact on economic development. An accurate model must be developed for forecasting the value of agricultural imports since rapid changes in industry and economic policy affect the value of agricultural imports. Conventionally, the ARIMA model has been utilized to forecast the value of agricultural imports, but it generally requires a large sample size and several statistical assumptions. Some studies have applied nonlinear methods such as the GM(1,1) and improved GM(1,1) models, yet neglected the importance of enhancing the accuracy of residual signs and residual series. Therefore, this study develops a novel two-stage forecasting model that combines the GM(1,1) model with genetic programming to accurately forecast the value of agricultural imports. Moreover, accuracy of the proposed model is demonstrated based on two agricultural imports data sets from the Taiwan and USA. (C) 2014 Elsevier B.V. All rights reserved.