Exchange rate forecasting with Artificial Intelligence

Abstract

This study concerns the problem of forecasting the exchange rate between the official currency of EU member states, Euro and Albanian Lek, aiming to identify the best predictive model for financial time series future trend prediction. We compare the forecasting performance of linear and nonlinear forecasting models using monthly data for the period between January 2002 until January 2022. We discuss various forecasting approaches, including an Autoregressive Integrated. Moving Average model, a Nonlinear Autoregressive Neural Network model, a BATS model and Exponential Smoothing on the collected data and compare their accuracy using error term measuring indicators, choosing the model with the lowest Mean Absolute Percentage Error value. Finding the most accurate forecasting model would help improve monetary and fiscal politics, as well as orient future personal investments.

Keywords

• NARNN
• ARIMA
• Artificial Intelligence
• Time series forecasting