Currency Exchange Rate Forecasting Using Artificial Neural Networks Backpropagation Method

Currency Exchange Rate Forecasting Using Artificial Neural Networks Backpropagation Method

Difana Meilani (Andalas University, Indonesia) and Ivan Richardo (Andalas University, Indonesia)
Copyright: © 2012 |Pages: 20
DOI: 10.4018/jgc.2012070102
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Since 1997, the rupiah currency has a tendency to change at any time since the economic crisis that hit Indonesia. One of the most widely traded currencies in the international exchange market is the U.S. dollar. This paper forecasts the exchange rate by using back propagation neural networks. Variables that affecting currency exchange rates is inflation, gross national product and interest rates. After performing data processing with the help of software forecasting results and forecasting program, it is displayed online by using PHP to construct the webpage.
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Back Propagation Training

As with other neural networks, the network feed forward training is done in order to calculate the weights so that at the end of the training will be obtained good weights. During the training process, the weights iteratively adjusted to minimize error that occurs.

Error is calculated based on the mean squared error (MSE). The mean squared error is also used as the basis for calculating the performance of the activation function. Most of the training for feed forward network using a gradient of activation function to determine how to adjust the weights in order to minimize the performance. This gradient is determined by using a technique called back propagation.

Basically, the standard back propagation training algorithm will move the weight with a negative gradient direction. The basic principle of back propagation algorithm is to improve the network weights with the direction that makes the activation function to be falling rapidly

Back propagation training includes three phases as follows.

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