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A Hybrid GA-Based SVM Model for Foreign Exchange Market Tendency Exploration

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 107)

Forecasting foreign exchange rates has been regards as one of the most challenging application of modern time series forecasting (Yu et al., 2005f ). Thus, numerous models have been developed to provide the investors with more precise predictions. Recently, artificial intelligence, such as artificial neural networks (ANN), has been widely applied to solve foreign exchange rates forecasting problems. Literature documenting the research shows the ANN has a powerful capability to predict foreign exchange rates. The previous chapters provide some literature reviews. Interested readers can be referred to the Chapter 1 and other chapters for more details about foreign exchange rates prediction. Two recently good surveys about foreign exchange rates prediction with ANN can be referred to Huang et al. (2004a, 2006) and Yu et al. (2005e) for more literature review.

The main motivation of this chapter is to propose a new hybrid intelligent data mining approach integrating SVM with GA for exploring foreign exchange market tendency and to test the predictability of the proposed hybrid intelligent model by comparing it with statistical models and neural network models. The rest of the chapter is organized as follows. The next section will describe the formulation process of the proposed hybrid intelligent data mining model in detail. In Section 9.3, we give an experiment scheme and report the experimental results. For comparison, the similarities and difference of three hybrids model proposed by this part are presented in Section 9.4. And Section 9.5 concludes this chapter.

Keywords

Linear Discriminant Analysis Foreign Exchange Foreign Exchange Rate Linear Discriminant Analysis Model BPNN Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

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