Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Establishment of the Two Single Models
  2.1. Data Selection
  2.2. Seasonal Index Model
  2.3. RBF Model
 3. Establishment of combination Forecasting Model
  3.1. Calculation of the Weight
  3.2. Combination Forecasting
 4. Conclusion
 References

키워드

저자

• Lihua Yang [ School of Economics and Management, Hubei University of Automotive Technology, Shiyan, 442002,China ]
• Baolin Li [ School of Economics and Management, Hubei University of Automotive Technology, Shiyan, 442002,China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605