Prediction of complaints at civil aviation airports based on ARMA model

Wenchang Lv; Xinghui Zhu

doi:10.1117/12.3009210

11 October 2023 Prediction of complaints at civil aviation airports based on ARMA model

Wenchang Lv, Xinghui Zhu

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129180K (2023) https://doi.org/10.1117/12.3009210
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

The civil aviation industry is the fundamental industry of the national economy, and the good operation of airports, the reduction and resolution of complaints are important aspects of the development of civil aviation transportation. Based on basic data such as the number of civil aviation airport complaints in the past 10 years, this article proposes a modeling and prediction method for airport complaint volume based on ARMA model, and uses the AIC (Akaike Information Criterion, AIC) to determine the optimal order of the model. The prediction results show that the ARMA Model can still achieve high-precision prediction even when the order is not high, and the prediction results of (8, 1) and other 9 orders and above are consistent with actual data. The prediction results reflect the seasonal changes in civil aviation airport complaints, reflect the seasonal characteristics of civil aviation transportation, and have a high degree of confidence. Compared with the Least Square (LS) method and Gray Model (GM) prediction, the prediction method based on ARMA Model is more reasonable and has prominent advantages.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenchang Lv and Xinghui Zhu "Prediction of complaints at civil aviation airports based on ARMA model", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129180K (11 October 2023); https://doi.org/10.1117/12.3009210

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