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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chuanrui Fu |
| Copyright Year | 2010 |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 277160 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424453252 |
| DOI | 10.1109/ICMSS.2010.5576363 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Exchange rates Time series analysis Artificial neural networks Predictive models Forecasting |
| Content Type | Text |
| Resource Type | Article |
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