The forecasting concept can be expressed as predicting a variable’s future values using various methods under certain assumptions. In the estimations made using the time series, the value of a variable in the current period and the past period values are used. In the time series analysis, past time values of the variable to be estimated constitute the model’s independent variables. Learning from data, generalizing, working with very large samples, etc. are essential Artificial Neural Networks (ANN) methodology features. ANN methodology, which provides significant advantages thanks to these features, has vast usage possibilities in prediction modeling as in other fields. ANN has become one of the time series estimations’ methods since the late 1980s. This study handles an artificial neural network learning paradigm to estimate the exchange rate for economic targets. This paradigm has not prior conditions such as normal distribution or stationary. Therefore it has been frequently preferred in time-series analyses. The present study predicts the Turkish Lira’s value against the U.S. Dollar by using an ANN methodology. Our study is built upon purchasing power parity and the different researches dealing with exchange rate predictions. Our primary purpose is to minimize the error between the expected output of the network and the output it produces. This study uses an Artificial Neural Network model that builds upon the MLP to estimate the exchange rates. We first define the MLP with one hidden layer for this model, using monthly data from 2000 to 2019. In contrast, the independent variables are interest rates, Gross Domestic Product, and Consumer Price Index data for Turkey and the United States of America. We prefer batch training to train the ANN in SPSS Neural Networks 17.0 software. Sixty percent of the data were used in training, 25 percent in the testing, and 10 percent in the holdout, of the artificial neural networks, respectively. According to research results, the performance of ANN is relatively higher. While the relative error in the training set is 1 percent, the test set’s relative error is below 1 percent (0,007).

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