In:
Journal of Systems Science and Information, Journal of Systems Science and Information (JSSI), Vol. 6, No. 4 ( 2018-09-26), p. 289-301
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.
Type of Medium:
Online Resource
ISSN:
2512-6660
DOI:
10.21078/JSSI-2018-289-13
Language:
Unknown
Publisher:
Journal of Systems Science and Information (JSSI)
Publication Date:
2018
detail.hit.zdb_id:
2891525-2
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