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Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 431512, 12 pages
Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method
Department of Management Science and Information System, School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
Received 28 August 2012; Accepted 3 October 2012
Academic Editor: Carlo Piccardi
Copyright © 2012 Yukun Bao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citations to this Article [5 citations]
The following is the list of published articles that have cited the current article.
- Zhongyi Hu, Yukun Bao, and Tao Xiong, “Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms,” The Scientific World Journal, vol. 2013, pp. 1–10, 2013.
- Zong-chang Yang, “Modeling and Forecasting Monthly Passenger-Load Movement Based on the Elliptic Orbit Algorithmic Model,” Journal of Computing in Civil Engineering, pp. 04014031, 2014.
- Qisheng Yan, Shitong Wang, and Bingqing Li, “Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction,” Discrete Dynamics in Nature and Society, vol. 2014, pp. 1–10, 2014.
- Wei Ming, Yukun Bao, Zhongyi Hu, and Tao Xiong, “Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models,” The Scientific World Journal, vol. 2014, pp. 1–14, 2014.
- Uneb Gazder, and Nedal T. Ratrout, “A new logit-artificial neural network ensemble for mode choice modeling: a case study for border transport,” Journal of Advanced Transportation, 2015.