Forecasting of foreign currency exchange rate using neural network
subject: Artificial neural network; Back propagation algorithm; Forecasting; Foreign exchange rate and training function
description: Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different training algorithms. The model was trained using historical data to predict four foreign currency exchange rates against Indian Rupee. The forecasting performance of the proposed system is evaluated by using statistical metric and compared. From the results, it is confirmed that the new approach provided an improve technique to forecast foreign exchange rate. It is also an effective tool and significantly close prediction can be made using simple structure. Among the five models, Levenberg-Marquardt based model outperforms than other models and attains comparable results. It also demonstrates the power of the proposed approach and produces more accurate prediction. In conclusion, the proposed scheme can improve the forecasting performance significantly when measured on three commonly used metrics.
creator: Kumar Chandar S.; Sumathi M.; Sivanandam S.N.
source: International Journal of Engineering and Technology, Vol-7, No. 1, pp. 99-108.
publisher: Engg Journals Publications
date: 2015-01-01
rights: Restricted Access
relation: ISSN: 23198613
format: Online
language: English
type: Article
coverage: Kumar Chandar S., Christ University, India, Madurai Kamaraj University, Madurai, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, India