A new multiscale decomposition ensemble approach for forecasting exchange rates |
| |
Institution: | 1. University of Glasgow Business School, University of Glasgow, Adam Smith Building, Glasgow G12 8QQ, UK;2. Pattern Recognition Laboratory, Department of Computer Engineering & Informatics, University of Patras, 26500 Patras, Greece;3. Andreas Karathanasopoulos Royal Docklands Business School, University of East London, UK;1. Orissa Engineering College, Bhubaneswar, India;2. Multidisciplinary Research Cell, Siksha ''O'' Anusandhan University, Bhubaneswar, India |
| |
Abstract: | Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates. |
| |
Keywords: | Exchange rates forecasting Variational mode decomposition Support vector regression Support vector neural network Ensemble learning |
本文献已被 ScienceDirect 等数据库收录! |
|