System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 9
2773-2778,2784

Research on interval prediction of air cargo demand based on decomposition integration

Li Zhi1
Bai Juncheng2
1. School of Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
2. School of Economics & Management, Xidian University, Xi'an 710071, China
DOI: 10.19734/j.issn.1001-3695.2022.02.0059

Abstract

Air cargo is an important strategic resource of country and plays an indispensable role in domestic and international trade. Scientific forecasting of air cargo demand is an important basis for airlines to make infrastructure planning and overall investment decisions. Aiming at the uncertainty of air cargo volume data, this paper introduced Bootstrap method for uncertainty estimation and proposed an interval prediction method based on decomposition integration from the practical needs. Specifically, this paper decomposed the historical data by seasonal and trend decomposition using loess(STL) method firstly, then forecasted the trend and seasonal components by support vector regression(SVR) and seasonal autoregressive integrated moving average(SARIMA), respectively. Thirdly, this paper extracted and resampled the white noise component by Bootstrap method. Finally, the prediction results were integrated and reconstructed with the processed white noise to quantify uncertainty using quantile construction intervals. The experimental results of cargo data from two hub airports in China show that the constructed interval can effectively quantify the uncertainty in combination with the predicted results, which provides a novel research idea for probabilistic interval prediction.

Foundation Support

国家自然科学基金资助项目(72161022)
甘肃省自然科学基金项目(20JR5RA394)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.02.0059
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: System Development & Application
Pages: 2773-2778,2784
Serial Number: 1001-3695(2022)09-033-2773-06

Publish History

[2022-04-21] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

李智, 白军成. 基于分解集成的航空货运需求区间预测研究 [J]. 计算机应用研究, 2022, 39 (9): 2773-2778,2784. (Li Zhi, Bai Juncheng. Research on interval prediction of air cargo demand based on decomposition integration [J]. Application Research of Computers, 2022, 39 (9): 2773-2778,2784. )

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  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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