Comparing ANN Based Models with ARIMA for Prediction of Forex Rates

In the dynamic global economy, the accuracy in forecasting the foreign currency exchange (Forex) rates or at least predicting the trend correctly is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. In this paper, we developed and investigated three Artificial Neural Network (ANN) based forecasting models using Standard Backpropagation (SBP), Scaled Conjugate Gradient (SCG) and Backpropagation with Baysian Regularization (BPR) for Australian Foreign Exchange to predict six different currencies against Australian dollar. Five moving average technical indicators are used to build the models. These models were evaluated using three performance metrics, and a comparison was made with the best known conventional forecasting model ARIMA. All the ANN based models outperform ARIMA model. It is found that SCG based model performs best when measured on the two most commonly used metrics and shows competitive results when compared with BPR based model on the third indicator. Experimental results demonstrate that ANN based model can closely forecast the forex market.