Forecasting exchange rate using deep belief networks and conjugate gradient method

Abstract

Forecasting exchange rates is an important financial problem. In this paper, an improved deep belief network (DBN) is proposed for forecasting exchange rates. By using continuous restricted Boltzmann machines (CRBMs) to construct a DBN, we update the classical DBN to model continuous data. The structure of DBN is optimally determined through experiments for application in exchange rates forecasting. Also, conjugate gradient method is applied to accelerate the learning for DBN. In the experiments, three exchange rate series are tested and six evaluation criteria are adopted to evaluate the performance of the proposed method. Comparison with typical forecasting methods such as feed forward neural network (FFNN) shows that the proposed method is applicable to the prediction of foreign exchange rate and works better than traditional methods.