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FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID ECONOMETRIC-AI ENSEMBLE LEARNING APPROACH

Journal of Systems Science & Complexity 2008年第1期21卷 1-19页

作者： Lean YU Shouyang WANG Kin Keung LAI Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China. Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong China.

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 anal- ysis 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 en- semble 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 predic- tion 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.

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A hybrid econometric-AI ensemble learning model for Chinese foreign trade prediction

A hybrid econometric-AI ensemble learning model for Chinese ...

引用

7th International Conference on Computational Science (ICCS 2007)

作者： Yu, Lean Wang, Shouyang Lai, Kin Keung Chinese Acad Sci Inst Syst Sci Acad Math & syst Sci Beijing 100080 Peoples R China City Univ Hong Kong Dept Management Sci Kowloon Peoples R China

ISBN: (纸本)9783540725893

Due to the complexity of economic system, the interactive effects of economic variables or factors on Chinese foreign trade make the prediction of China's foreign trade extremely difficult. To analyze the relationship between economic variables and foreign trade, this study proposes a novel nonlinear ensemble learning methodology hybridizing nonlinear econometric model and artificial neural networks (ANN) for Chinese foreign trade prediction. In this proposed learning approach, an important econometrical model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of the economic variables on Chinese foreign trade from a multivariate analysis perspective. Then an ANN-based EC-VAR model is used to capture the nonlinear patterns hidden between foreign trade and economic factors. Subsequently, for introducing the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also incorporated into the nonlinear ANN-based EC-VAR model. Finally, all economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as another neural network inputs for ensemble prediction purpose. For illustration, the proposed ensemble learning methodology integrating econometric techniques and artificial intelligence (AI) methods is applied to Chinese export trade prediction problem.

关键词： artificial neural networks error-correction vector auto-regression hybrid ensemble learning foreign trade prediction

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