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A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting

Author

Listed:
  • Wei Yunjie

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China)

  • Sun Shaolong

    (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China)

  • Lai Kin Keung

    (International Business School, Shaanxi Normal University, Xi‘an, 710119, China)

  • Abbas Ghulam

    (School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China)

Abstract

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.

Suggested Citation

  • Wei Yunjie & Sun Shaolong & Lai Kin Keung & Abbas Ghulam, 2018. "A KELM-Based Ensemble Learning Approach for Exchange Rate Forecasting," Journal of Systems Science and Information, De Gruyter, vol. 6(4), pages 289-301, August.
    • Handle: RePEc:bpj:jossai:v:6:y:2018:i:4:p:289-301:n:1
    DOI: 10.21078/JSSI-2018-289-13
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