Earticle

초록

영어

In this paper, we first comparative analysis the existing prediction methods. Based on the GM and ARMA, we propose a new combined forecasting model which integrated the advantage of the GM is suitable for medium and long term forecast, the GM algorithm is simple and the ARMA is suitable for short time forecast. Moreover, we use the rail traffic data to verify this model. The results show that the combined forecasting model we proposed is of high forecast precision, and the combined forecasting model is better than the single forecasting model.

목차

Abstract
 1. Introduction
 2. Principle Introduction
  2.1. Grey Model
  2.2. ARMA Model
  2.3. Combination Forecasting Model
 3. Cases of Application of the Model
  3.1 Data Sets used by the Model
  3.2. GM (1,1) Model Predictions
  3.3. ARMA Model Predictions
  3.4. Combination Forecasting Model
  3.5. Prediction
 4.Conclusions
 References

키워드

저자

• Yunjian Jia [ College of Communication Engineering, Chongqing University, China ]
• Peihua He [ College of Communication Engineering, Chongqing University, China ]
• Shuguang Liu [ Institute of Electronic Information & Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China ]
• Lei Cao [ Institute of Electronic Information & Technology, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605