基于二次分解策略和模糊时间序列模型的航空客运需求预测研究

doi:10.16381/j.cnki.issn1003-207x.2019.1100

Abstract

Abstract: Improving the accuracy of air passenger demand forecasting plays a crucial role in the development of airlines and the whole air transport system,which is of great political and economic significance for all of society but is still a challenging task.In previous studies, individual decomposition strategy has been adopted generally to deal with the complex features inair passenger demand series, so as to improve the prediction performance of the hybrid model. However, the traditional decomposition strategy has some drawbacks such as incomplete feature extraction and the inherent defects in the decomposition method, which lead to the inadequate improvement of the prediction effect of the hybrid model.Therefore, a method for air passenger demand forecasting based on a dual decomposition strategy and a fuzzy time series modelis proposed.Firstly, a seasonal adjustment model (X12-ARIMA) is applied to decompose the original series into a seasonal component and a seasonally adjusted series. Then the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the seasonally adjusted series into several intrinsic mode functions (IMFs) with different time scales and a residue.After that, the FTS model with fuzzy c-means algorithm (FCM) partitioning intervals is used to forecast each IMF component, the residue and the seasonal component. Finally, the prediction of air passenger traffic is obtained by integrating the above results.Case studies utilizing monthly air passenger demand data collected from Shanghai Pudong International Airport, Chengdu Shuangliu International Airport and Shenzhen Bao'an International Airport are employed as examples. The empirical results show that the dual decomposition strategy proposed in this paper is significantly better than the traditional decomposition strategy, and the proposed model outperforms all of the considered comparison models. The proposed model can be used to improve the accuracy of air passenger demand prediction.

Key words: forecast of air passenger demand, seasonal adjustment, improved complete ensemble empirical mode decomposition with adaptive noise, fuzzy c-means algorithm, fuzzy time series model

CLC Number:

F562.8

LIANG Xiao-zhen, WU Zhi-kun, YANG Ming-ge, WANG Shou-yang. Air Passenger Demand Forecasting Based on a Dual Decomposition Strategy and Fuzzy Time Series Model[J]. Chinese Journal of Management Science, 2020, 28(12): 108-117.

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Air Passenger Demand Forecasting Based on a Dual Decomposition Strategy and Fuzzy Time Series Model

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