Bootstrap Methods for Foreign Currency Exchange Rates Prediction
Haibo He, Xiaoping Shen · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
This paper presents the research of using boot-strap methods for time-series prediction. Unlike the traditional single model (neural network, support vector machine, or any other types of learning algorithms) based time-series prediction, we propose to use bootstrap methods to construct multiple learning models, and then use a combination function to combine the output of each model for the final predicted output. In this paper, we use the neural network model as the base learning algorithm and applied this approach to the foreign currency exchange rate predictions. Six major foreign currency exchange rates including Australia Dollars (AUD), British Pounds (GBP), Canadian Dollars (CAD), European Euros (EUR), Japanese Yen (JPY) and Swiss Francs (CHF) are used for prediction (base currency is US Dollar). Simulations on the most recently available exchange rate data (January 01, 2003 to December 27, 2006) on both daily prediction and weekly prediction indicate that the proposed method can significantly improve the forecasting performance compared to the traditional single neural network based approach.