Research on exchange rate forecasting based on deep belief network

Abstract

Exchange rate forecasting has always been a research hot spot of international finance studies. Deep belief network (DBN) model of deep learning is a new method of predicting the exchange rate data, and the designing of DBN structure and the learning rules of parameters are the most important parts of DBN model. The paper firstly divides the time series data into training and testing sets. By optimizing the DBN parameters, the paper analyses the results of the training analysis and answers how to do node setting. Then, the paper adjusts the number of hidden nodes, inputs nodes and hidden layers, and by using multiple variance analysis, it determines the sensitive range of the node. Finally, the experiments of INR/USD and CNY/USD have proved that compared with the FFNN model, the improved DBN model could better forecast the exchange rate.