Exchange rates forecasting with decomposition-clustering-ensemble learning approach

被引：0

作者：

Sun, Shaolong ^{[1
,2
,3
]}

Wei, Yunjie ^{[2
,4
]}

Wang, Shouyang ^{[2
,3
,4
]}

机构：

[1] School of Management, Xi'an Jiaotong University, Xi'an,710049, China

[2] Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing,100190, China

[3] School of Economics and Management, University of Chinese Academy of Sciences, Beijing,100190, China

[4] Center for Forecasting Science, Chinese Academy of Sciences, Beijing,100190, China

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2022年 / 42卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Finance - Forecasting - K-means clustering - Cluster analysis - Learning systems;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This paper proposes a new EEMD-LSSVR-K-based decomposition-clustering-ensemble learning approach for foreign exchange rates forecasting by integrating ensemble empirical mode decomposition (EEMD), least square support vector regression (LSSVR) and K-means clustering algorithm. Clustering strategy is used to extend the fixed-weighted meta-synthetic in decomposition-ensemble learning approach to weighted with local data characteristics meta-synthetic. Our proposed approach can effectively solve the shortcoming of fixed-weighted meta-synthetic in decomposition-ensemble learning approach. Meanwhile, our proposed approach is applied to four type exchange rates forecasting. The empirical results show that our proposed approach significantly improves the level and directional accuracy of exchange rates forecasting, and verify the importance of clustering strategy in decomposition-ensemble learning approach. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.

引用

页码：664 / 677