Forecasting China's foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach

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	Forecasting China's foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach
	Yu, Lean 1; Wang, Shouyang1 ; Lai, Kin Keung 2
	2008-03-01
发表期刊	JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
ISSN	1009-6124
卷号	21 期号:1 页码:1-19
摘要	Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear analysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for ensemble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume prediction problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.
关键词	artificial neural networks error-correction vector auto-regression foreign trade prediction hybrid ensemble learning kernel-based method support vector regression
语种	英语
WOS研究方向	Mathematics
WOS类目	Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000255240700001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/6740
专题	系统科学研究所
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China 2.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Wang, Shouyang,Lai, Kin Keung. Forecasting China's foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach[J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,2008,21(1):1-19.
APA	Yu, Lean,Wang, Shouyang,&Lai, Kin Keung.(2008).Forecasting China's foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach.JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY,21(1),1-19.
MLA	Yu, Lean,et al."Forecasting China's foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach".JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 21.1(2008):1-19.