Long-Term Forecasting of Euro-Dollar Exchange Rates Using the ARIMA Model and Multilayer Perceptron

Forecasting exchange rates is a complex problem due to the inherent volatility and complex dynamics of exchange rates.Traditional forecasting models such as ARIMA often cannot capture these complexities especially for long-term forecasts.The objective of this study is to develop an accurate forecasting model for long-term exchange rates.A data set of eurodollar exchange rates from 2017 to 2022 was used for the present analysis.ARIMA and MLP models were developed and their performances were compared; the optimized MLP model equipped with 11 input neurons derived from significant lags achieved a scaled mean absolute error (MASE) of 0.75 on the test data while the MLP model significantly outperformed the ARIMA model, demonstrating its ability to capture underlying patterns and trends in the exchange rate data.The optimized MLP model also provided a 365-day forecast for 2023 exchange rates.The results of this study suggest that MLP models are a promising tool for long-term forecasting of exchange rates.Their ability to capture complex nonlinear relationships and adapt to changing market conditions makes them well suited for this challenging task.