文章基本信息

标题：Neural Network Based Forecasting of Foreign Currency Exchange Rates
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作者：S. Kumar Chandar ; Dr. M. Sumathi ; Dr S. N. Sivanandam 等
期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2014
卷号：6
期号：06
页码：202-206
出版社：Engg Journals Publications
摘要：The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other major currencies, Pound Sterling, US Dollar, Euro and Japanese Yen are forecast by the trained neural networks. The neural network was trained by three different learning algorithms using historical data to find the suitable algorithm for prediction. The forecasting performance of the proposed system is evaluated using three statistical metrics and compared. The results presented here demonstrate that significantly close prediction can be made without extensive knowledge of market data.
关键词：Artificial Neural Network; Back Propagation Algorithm; Forecasting; Foreign Exchange Rate and Training Function.