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Wei Yunjie1; Sun Shaolong1; Lai Kin Keung2; Abbas Ghulam3
Source Publicationjournalofsystemsscienceandinformation
AbstractIn 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.
Document Type期刊论文
Recommended Citation
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.
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