Exploring the use of water cycle optimization algorithm for foreign exchange prediction

Abstract

The aim of this paper is to model a network and predict the exchange price of United States Dollar to Indian Rupees using daily exchange rates from Dec 18, 1991-Jul 19, 2007. In this paper, Water Cycle Optimization (WCA) technique has been used to optimize the Artificial Neural Network (ANN) for Foreign Exchange prediction on the basis of their predictive performance. The performance metrics considered for the evaluation of the models are root mean square error (RMSE) and mean absolute error (MAE). The tabulated outcome shows the efficiency of the model over other popular models.