Forecasting Foreign Exchange Rate Using Robust Lagueree Neural Network

In this article, single layer based functional basis neural network has been used for foreign exchange rate prediction. In general, foreign exchange rate problem is one of the most complex problems with high non linearity and data irregularity. From many studies it is found that foreign exchange rate prediction always fluctuates with economic growth, interest rate and influence rates and therefore it is very difficult for researcher to predict foreign exchange rate. Therefore, foreign exchange rate prediction becomes a challenging task for every researcher for both academic and industrial communities. In this article two type of single layer functional link artificial neural network Functional-link Artificial Neural Network (FLANN) and Laguerre Polynomial Equation ( LAPE) were applied to forecast foreign exchange data. With high data irregularity, FLANN and LAPE both the models provide extremely precise outcome for complex time series model. The single layered based functional basis neural network architectures results matched strongly with ARIMA with very less Mean Square Error (MSE). From the Simulation study, single layer based functional basis neural network models provide improved results compare to ARIMA model with less Root Mean Square Error (RMSE) and performs as universal approximator.