원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.2
2016.02
pp.215-226
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보