The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network

To effectively predict auto sales and improve the competitiveness of automotive enterprise, the characteristics of actual auto sales were analyzed, owing to the seasonal fluctuations and the nonlinearity of monthly sales, the combination forecasting model based on seasonal Index and RBF neural network was proposed. The weights of the two single models were computed using mean absolute percentage error and the sum of square error respectively, the result shows that mean absolute percentage error is more effective. Finally, the prediction accuracy of different models was compared based on the criteria of MAPE and RMSE, and the effectiveness of the method was proved, the proposed model can take advantage of the strengths of the two single models, the results indicate that the combination forecasting model suitable for auto sales has high prediction accuracy, which can provide a certain reference to auto sales forecasting.