Neural fuzzy forecasting of the China Yuan to US dollar exchange rate - A swarm intelligence approach

Exchange rate fluctuation has a significant effect on the risk of marketing business, economic development and financial stability. Accurate prediction for exchange rate may reduce commercial and economic risk arisen by exchange rate fluctuation. In this study, we propose an intelligent approach to the forecasting problem of the CNY-USD exchange rate, where a neuro-fuzzy self-organizing system is used as the intelligent predictor. For learning purpose, a novel hybrid learning method is devised for the intelligent predictor, where the well-known particle swarm optimization (PSO) algorithm and the recursive least squares estimator (RLSE) algorithm are involved. The proposed learning method is called the PSO-RLSE-PSO method. Experiments for time series forecasting of the CNY-USD exchange rate are conducted. For performance, the intelligent predictor is trained by several different methods. The experimental results show that the proposed approach has excellent forecasting performance.