Exchange Rate Forecasting Using Classifier Ensemble

: In this paper, we investigate the impact of the non-numerical information on exchange rate changes and that of ensemble multiple classifiers on forecasting exchange rate between U.S. dollar and Japanese yen. We first engage the fuzzy comprehensive evaluation model to quantify the nonnumerical fundamental information. We then design a single classifier, addressing the impact of exchange rate changes associated with this information. In addition, we also propose other different classifiers in order to deal with the numerical information. Finally, we integrate all these classifiers using a support vector machine (SVM). Experimental results showed that our ensemble method has a higher degree of forecasting accuracy after adding the non-numerical information.

• Type: Journal
• Year: 2009
• Where: ICONIP
• Authors: Zhi-Bin Wang, Hong-Wei Hao, Xu-Cheng Yin, Qian Liu, Kaizhu Huang
• Keywords: Exchange Rate, Exchange Rate Changes, ICONIP 2009, Information, Information Technology