By Topic

Study of SVM-Based Air-Cargo Demand Forecast Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hong-jun Heng ; Dept. of Comput. Sci. & Technol., Civil Aviation Univ. of China, Tianjin, China ; Bing-zhong Zheng ; Ya-jing Li

This paper analyzed some existing problems of the present air-cargo forecast methods. Then it established the SVM (support vector machine) model for air-cargo demand forecasting. Taking the historical statistical data of Beijing to Shanghai cargo volumes from Jan-2005 to Mar-2006 as fitting and forecasting specimens, we can obtain the prediction model to optimize, which was compared with that of Brown cubic exponential smoothing, by analyzing fitting and forecasting effect of model for different number of input nodes respectively. The result showed that the fitting effect by the model based on support vector machine was better than that of Brown. The former has a higher forecasting accuracy.

Published in:

Computational Intelligence and Security, 2009. CIS '09. International Conference on  (Volume:2 )

Date of Conference:

11-14 Dec. 2009