Next Article in Journal
Cross-Regional Deep Learning for Air Quality Forecasting: A Comparative Study of CO, NO2, O3, PM2.5, and PM10
Previous Article in Journal
Non-Negative Forecast Reconciliation: Optimal Methods and Operational Solutions
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers

by
Efstratios Bilis
1,
Theophilos Papadimitriou
2,*,
Konstantinos Diamantaras
1 and
Konstantinos Goulianas
1
1
Department of Information and Electronic Engineering, International Hellenic University, 57400 Thessaloniki, Greece
2
Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
*
Author to whom correspondence should be addressed.
Forecasting 2025, 7(4), 65; https://doi.org/10.3390/forecast7040065
Submission received: 6 September 2025 / Revised: 23 October 2025 / Accepted: 24 October 2025 / Published: 29 October 2025
(This article belongs to the Section Forecasting in Economics and Management)

Abstract

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 exchange rates; time series forecasting; deep learning; Transformers

Share and Cite

MDPI and ACS Style

Bilis, E.; Papadimitriou, T.; Diamantaras, K.; Goulianas, K. EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers. Forecasting 2025, 7, 65. https://doi.org/10.3390/forecast7040065

AMA Style

Bilis E, Papadimitriou T, Diamantaras K, Goulianas K. EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers. Forecasting. 2025; 7(4):65. https://doi.org/10.3390/forecast7040065

Chicago/Turabian Style

Bilis, Efstratios, Theophilos Papadimitriou, Konstantinos Diamantaras, and Konstantinos Goulianas. 2025. "EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers" Forecasting 7, no. 4: 65. https://doi.org/10.3390/forecast7040065

APA Style

Bilis, E., Papadimitriou, T., Diamantaras, K., & Goulianas, K. (2025). EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers. Forecasting, 7(4), 65. https://doi.org/10.3390/forecast7040065

Article Metrics

Back to TopTop