Exploring the use of Water Cycle Optimization Algorithm for Foreign Exchange Prediction
Arup Kumar Mohanty1, Debahuti Mishra2
1Arup Kumar Mohanty, Department of Computer Science&Information Technology, Siksha ‘O’ AnusandhanDeemed to be University, Bhubaneswar, Odisha, India
2Debahuti Mishra, Department of Computer Science and Engineering, Siksha ‘O’ AnusandhanDeemed to be University, Bhubaneswar, Odisha, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 August 2019 | PP: 680-684 | Volume-8 Issue-10, August 2019 | Retrieval Number: J87930881019/2019©BEIESP | DOI: 10.35940/ijitee.J8793.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: ANN; Forex; Machine Learning; Prediction; WCA
Scope of the Article: Machine Learning