Predicting the exchange rate of the Turkish lira against the US dollar using artificial neural networks
Keywords:
exchange rate, ANN, TurkeyAbstract
The study discusses how to design an artificial neural network to predict the exchange rate of the Turkish lira against the US dollar. Using monthly historical data of the exchange rate and a set of other important macroeconomic indicators (base interest rate and monthly inflation rate). The model relied on a reverse propagation algorithm to train the network, with training repeated over several cycles amounting to 10,000 cycles to ensure improved accuracy of predictions. After the model was built, its performance was evaluated using criteria such as the mean absolute error (MAE) and the square root of the mean error (RMSE) to measure the accuracy of the predictions. Finally, the model was tested. The results showed the strength of the designed neural networks in predicting the exchange rate, where the expected values of the network outputs matched significantly with the actual values, which highlights their effectiveness in providing accurate and reliable predictive insights.
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