An integrated forecasting model for air passenger traffic in China based on singular spectrum analysis
LIANG Xiaozhen1, QIAO Han2, WANG Shouyang2,3, ZHANG Xun3
1. School of Management, Shanghai University, Shanghai 200444, China; 2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; 3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Abstract：Considering the noise contained in time-series data and the sometimes poor forecasting performance of single model, this paper proposes an integrated model based on singular spectrum analysis (SSA) for Chinese annual air passenger traffic forecasting. In the modeling process, the original time series was first decomposed into several different components using SSA, and the main components were extracted to reconstruct a new time series with the noise removed. Then, the reconstructed time series was predicted with three single models respectively, including autoregressive integrated moving average (ARIMA), support vector regression (SVR) and Holt-Winters method (HW). After that, the weighted average method (WA) was used to integrate the prediction results of the three single models above. The performance of the proposed model was compared with those of three single models (ARIMA, SVR, and HW), corresponding models based on another decomposition method (empirical mode decomposition, EMD) and another integrated forecasting method (simple average method, SA). The results suggested that the proposed model could achieve better forecasting performance than the remaining ones. Finally, annual air passenger traffic in China from 2014 to 2016 was predicted using the proposed model.
梁小珍, 乔晗, 汪寿阳, 张珣. 基于奇异谱分析的我国航空客运量集成预测模型[J]. 系统工程理论与实践, 2017, 37(6): 1479-1488.
LIANG Xiaozhen, QIAO Han, WANG Shouyang, ZHANG Xun. An integrated forecasting model for air passenger traffic in China based on singular spectrum analysis. Systems Engineering - Theory & Practice, 2017, 37(6): 1479-1488.
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