摘要
A decomposition clustering ensemble(DCE)learning approach is proposed for forecasting foreign exchange rates by integrating the variational mode decomposition(VMD),the selforganizing map(SOM)network,and the kemel extreme leaming machine(KELM).First,the exchange rate time series is decomposed into N subcomponents by the VMD method.Second,each subcomponent series is modeled by the KELM.Third,the SOM neural network is introduced to cluster the subcomponent forecasting results of the in-sample dataset to obtain cluster centers.Finally,each cluster's ensemble weight is estimated by another KELM,and the final forecasting results are obtained by the corresponding clusters'ensemble weights.The empirical results illustrate that our proposed DCE learning approach can significantly improve forecasting performance,and statistically outperform some other benchmark models in directional and level forecasting accuracy.
基金
supported by the National Natural Science Foundation of China under Project No.71801213 and No.71642006
the Hong Kong R&D Projects under Project No.7004715
the Research Grant Council of Hong Kong under Project No.2016-3-56.
作者简介
corresponding author:Kin Keung Lai,International Business School Shaanxi Normal University,Xi'an,710119,China.E-mail address:mskklai@0utlook.com
