EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers

doi:10.3390/forecast7040065

EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers

2025-10-29

1

PRE

AI

E

Efstratios Bilis

T

Théophilos Papadimitriou *

K

Konstantinos Diamantaras

K

Konstantinos Goulianas

DOI：10.3390/forecast7040065

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Abstract

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This study introduces a Transformer-based forecasting tool termed EXPERT (EXchange rate Prediction using Encoder Representation from Transformers) and applies it to exchange rate forecasting. We developed and trained a Transformer-based forecasting model, then evaluated its performance on nine currency pairs with various characteristics. Finally, we benchmarked its effectiveness against six established forecasting models: Linear Regression, Random Forest, Stochastic Gradient Descent, XGBoost, Bagging Regression, and Long Short-Term Memory. Our dataset covers the period from 1999 to 2022. The models were evaluated for their ability to predict the next day's closing price using three performance metrics. In addition, the EXPERT system was evaluated on its ability to extend forecast horizons and as the core of a trading strategy. The model's robustness was further evaluated using the Multiple Comparisons with the Best (MCB) metric on five dataset samples.

Keywords:

exchange rates

time series forecasting

deep learning

Transformers

EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers

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