Abstract

In order to improve the accuracy of grain production forecasting, this study proposed a new combination forecasting model, the model combined stepwise regression method with RBF neural network by assigning proper weights using inverse variance method. By comparing different criteria, the result indicates that the combination forecasting model is superior to other models. The performance of the models is measured using three types of error measurement, which are Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (Theil IC) and Root Mean Squared Error (RMSE). The model with smallest value of MAPE, Theil IC and RMSE stands out to be the best model in predicting the grain production. Based on the MAPE, Theil IC and RMSE evaluation criteria, the combination model can reduce the forecasting error and has high prediction accuracy in grain production forecasting, making the decision more scientific and rational.

Keywords:

Combination forecasting, grain production forecasting, RBF neural network, stepwise regression method,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.