A model with Fuzzy Granulation and Deep Belief Networks for exchange rate forecasting

In recent years, neural networks is increasingly adopted in the prediction of exchange rate. However, most of them predict a specific number, which can not help the speculators too much because small gap between the predicted values and the actual values will lead to disastrous consequences. In our study, our purpose is to present a model to forecast the fluctuation range of the exchange rate by combining Fuzzy Granulation with Continuous-valued Deep Belief Networks (CDBN), and the concept of "Stop Loss" is introduced for making the environment of our profit strategy close to the real foreign exchange trade market. The proposed model is applied to forecasting both Euro/US dollar and British pound/US dollar exchange rate in our experiments. Experimental results show that the proposed method is more profitable in the trading process than other typical models.