Forecasting China's foreign trade volume with a kernel-based hybrid econometric-AI ensemble learning approach
文献类型：期刊论文
| 作者 | Yu, Lean1; Wang, Shouyang1 ; Lai, Kin Keung2
|
| 刊名 | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
 |
| 出版日期 | 2008-03-01 |
| 卷号 | 21期号:1页码:1-19 |
| 关键词 | artificial neural networks error-correction vector auto-regression foreign trade prediction hybrid ensemble learning kernel-based method support vector regression |
| ISSN号 | 1009-6124 |
| 英文摘要 | 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. |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000255240700001 |
| 出版者 | SPRINGER |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/6739]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。