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EXPERT: EXchange Rate Prediction Using Encoder Representation from Transformers
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DOI：10.3390/forecast7040065.png)
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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.
Keyword:
exchange rates
time series forecasting
deep learning
Transformers
期刊
F
IF:
3.2
论文数:
62
被引数:
0
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