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An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting

Department of Electronic and Information Engineering, Tongji Zhejiang College, Jiaxing 314051, China
Whitman School of Management, Syracuse University, Syracuse, NY 13244, USA
Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
Author to whom correspondence should be addressed.
Algorithms 2017, 10(4), 139;
Received: 21 September 2017 / Revised: 9 December 2017 / Accepted: 12 December 2017 / Published: 14 December 2017
PDF [2054 KB, uploaded 14 December 2017]


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. View Full-Text
Keywords: air traffic forecasting; hybrid modeling; empirical mode decomposition; seasonal autoregressive integrated moving average air traffic forecasting; hybrid modeling; empirical mode decomposition; seasonal autoregressive integrated moving average

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Nai, W.; Liu, L.; Wang, S.; Dong, D. An EMD–SARIMA-Based Modeling Approach for Air Traffic Forecasting. Algorithms 2017, 10, 139.

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