An EMD-SARIMA-based modeling approach for air traffic forecasting

被引：8
作者：
Nai W. [1 ]
Liu L. [2 ]
Wang S. [1 ]
Dong D. [3 ]
机构：
[1] Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing
[2] Whitman School of Management, Syracuse University, Syracuse, 13244, NY
[3] Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai
来源：
Algorithms | 2017年 / 10卷 / 04期
关键词：
Air traffic forecasting; Empirical mode decomposition; Hybrid modeling; Seasonal autoregressive integrated moving average;
D O I：
10.3390/a10040139
中图分类号：
学科分类号：
摘要：
The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved. © 2017 by the authors.
引用
收藏
相关论文
共 41 条
  • [1] Laik M.N., Choy M., Sen P., Predicting airline passenger load: A case study, Proceedings of the 16th IEEE Conference on Business Informatics, (2014)
  • [2] Fildes R., Wei Y., Ismail S., Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures, Int. J. Forecast, 27, pp. 902-922, (2011)
  • [3] Yan K., Study on the forecast of air passenger flow based on SVM regression algorithm, Proceedings of the 1st International Workshop on Database Technology and Applications, (2009)
  • [4] Tseytlina T.O., Analysis of possibility of using neural network to forecast passenger traffic flows in Russia, Aviation, 11, pp. 28-35, (2007)
  • [5] Smith D.A., Sherry L., Decision support tool for predicting aircraft arrival rates from weather forecasts, Proceedings of the Integrated Communications Navigation and Surveillance Conference, (2008)
  • [6] Zhang Y., Zhang J., A hybrid model of neural network and grey theory for air traffic passenger volume forecasting, Key Eng. Mater, 439-440, pp. 818-822, (2010)
  • [7] Box G.E., Jenkins G.M., Reinsel G.C., Time Series Analysis: Forecasting and Control, pp. 1-96, (2008)
  • [8] Okutani I., Stephanedes Y.J., Dynamic prediction of traffic volume through Kalman filtering theory, Transp. Res. Part B Methodol, 18, pp. 1-11, (1984)
  • [9] Smith B.L., Demetsky M.J., Short-term traffic flow prediction: Neural network approach, Transp. Res. Rec, 1453, pp. 98-104, (1994)
  • [10] Vlahogianni E.I., Karlaftis M.G., Golias J.C., Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach, Transp. Res. Part C Emerg. Technol, 13, pp. 211-234, (2005)
12345