Foreign Exchange Rates Forecasting with Multiple Candidate Models: Selecting or Combining? A Further Discussion
From Chapter 4 to Chapter 12, nine typical foreign exchange rates forecasting models, including three single neural network models, three neural network hybrid models and three neural network ensemble models, are proposed from the perspectives of level estimation and direction exploration. However, in the development process of forecasting models, we often have to come up against two important dilemmas (Yu et al., 2005g): (1) Whether should we select an appropriate modeling approach for prediction purposes or should combine these different individual approaches into an ensemble forecast for the different/dissimilar models? (2) Whether should we select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches?
The motivation of this chapter is how to deal with the two dilemmas of selecting and combining for time series forecasting (in particular, foreign exchange rates forecasting problem) and meantime propose a solution to the two dilemmas. The rest of the chapter is organized as follows. The next section describes the procedure for dealing with the two dilemmas of selecting and combining in detail. To verify the effectiveness of the proposed procedures, a typical foreign exchange rate experiment is performed in Section 13.3. And Section 13.4 concludes this chapter and points out some future research directions.
KeywordsRoot Mean Square Error Artificial Neural Network Artificial Neural Network Model Ensemble Forecast Model Selection Criterion
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