期刊论文

Liu, John J.

英文

Transportation Letters

1942-7867

2015

7

2

73-79

10.1179/1942787514Y.0000000035

National Social Science Foundation of China [14BGL175]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71101100, 71301160]; CityU HK; CTTFS [9360142]; Center for Forecasting Science of the Chinese Academy of Sciences

本校为第一机构

The air transport industry crucially depends on traffic forecasting for supporting management decisions. In this study, a singular spectrum analysis (SSA)-based ensemble forecasting modeling approach is proposed. The original air passenger time series is first decomposed into three components: trend, seasonal oscillations, and irregular component. The trend is predicted by generalized regression neural network (GRNN), whereas seasonal oscillations are predicted by radial basis function networks (RNFNs). The empirical results of Hong Kong (HK) air passenger data show a significant improvement o...