FORECASTING THE EURO EXCHANGE RATE USING DEEP LEARNING ALGORITHMS AND MACHINE LEARNING ALGORITHMS

Abstract

Given that time series forecasts are of great importance in the financial world, the main objective of this study is to forecast Euro prices and examine the contribution of these forecasts to financial decision-making processes. Since the Euro is an important component of international trade and investment, accurate price forecasts are of strategic importance for many financial institutions and investors. In this study, we compare the performance of deep learning algorithms and classical machine learning methods for forecasting Euro prices: support vector machines (SVM), Extreme Gradient Boosting (XGBoost), long short-term memory (LSTM), and gated recurrent units (GRU). These methods represent different algorithms that are widely used in financial forecasting and give successful results. The dataset used in the study was divided into two parts: 80% training and 20% testing, and it is also indicated how each algorithm behaved during the training process and which parameters were chosen. The results are presented by comparing the performance of these algorithms, and it is found that the GRU algorithm provides better accuracy than the others. Therefore, the GRU algorithm was chosen to forecast Euro prices for the next 12 months, and the forecasting process was carried out. The results of this study are expected to provide an important perspective to financial decision-makers by comprehensively comparing the performance of deep learning and traditional approaches in Euro price forecasting. It also includes potential research avenues for future work and suggestions for the development of new methods in this area.