akelmbasedensemblelearningapproachforexchangerateforecasting

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	akelmbasedensemblelearningapproachforexchangerateforecasting
	Wei Yunjie2 ; Sun Shaolong 2; Lai Kin Keung 1; Abbas Ghulam 3
刊名	journalofsystemsscienceandinformation
	2018
卷号	000 期号:004 页码:289
ISSN号	1478-9906
英文摘要	In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM(Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import,export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.
语种	英语
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/38668]
专题	系统科学研究所
作者单位	1.陕西师范大学 2.中国科学院数学与系统科学研究院 3.中国科学院大学
推荐引用方式 GB/T 7714	Wei Yunjie,Sun Shaolong,Lai Kin Keung,et al. akelmbasedensemblelearningapproachforexchangerateforecasting[J]. journalofsystemsscienceandinformation,2018,000(004):289.
APA	Wei Yunjie,Sun Shaolong,Lai Kin Keung,&Abbas Ghulam.(2018).akelmbasedensemblelearningapproachforexchangerateforecasting.journalofsystemsscienceandinformation,000(004),289.
MLA	Wei Yunjie,et al."akelmbasedensemblelearningapproachforexchangerateforecasting".journalofsystemsscienceandinformation 000.004(2018):289.