Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm

Agriculture is the foundation of the national economy. Thus, an appropriate tool for forecasting agricultural output is very important for policy making. In this study, both modified background value calculation and use of a genetic algorithm to find the optimal parameters were adopted simultaneously to construct an improved GM(1,1) model (GAIGM(1,1)). The sample period of the forecasting models includes the annual values for the data of Taiwan’s agricultural output from 1998 to 2010. The mean absolute percentage error and the root mean square percentage error are two criteria with which to compare the various forecasting models results. Both in-sample and out-of-sample forecast performance results show that the GAIGM(1,1) model has highly accurate forecasting. Therefore, the GAIGM(1,1) model can raise the forecast accuracy of the GM(1,1) model, and it is suitable for use in modeling and forecasting of agricultural output.

► This study aimed to develop forecasting models for forecasting agricultural output. ► The GAIGM(1,1) is a good forecasting model for agricultural output forecasting. ► The GAIGM(1,1) can raise the forecast accuracy of the GM(1,1) model.