Exchange rate is considered as a highly nonlinear and non-stationary time series which can hardly be properly modeled and accurately predicted by traditional linear econometric models. This study attempts to propose an exchange rate ensemble learning paradigm called EMD-SVR. This methodology decomposes the original non-stationary and irregular exchange rate series into a finite and often small number of sub-signals by empirical mode decomposition (EMD). Then each sub-signal is modeled and forecasted by a Support Vector Regression (SVR). Finally the forecast of exchange rate is obtained by aggregating all prediction results of sub-signals. We verify the effectiveness and predictability of EMD-SVR using EUR/RMB time series as sample. The result shows that EMD-SVR has a strong forecasting ability and is remarkably superior to normal SVR.
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