Prediction of Currency Exchange Rate Based on Transformers

Abstract

The currency exchange rate is a crucial link between all countries related to economic and trade activities. With increasing volatility, exchange rate fluctuations have become frequent under the combined effects of global economic uncertainty and political risks. Consequently, accurate exchange rate prediction is significant in managing financial risks and economic instability. In recent years, the Transformer models have attracted attention in the field of time series analysis. Transformer models, such as Informer and TFT (Temporal Fusion Transformer), have also been extensively studied. In this paper, we evaluate the performance of the Transformer, Informer, and TFT models based on four exchange rate datasets: NZD/USD, NZD/CNY, NZD/GBP, and NZD/AUD. The results indicate that the TFT model has achieved the highest accuracy in exchange rate prediction, with an R2 value of up to 0.94 and the lowest RMSE and MAE errors. However, the Informer model offers faster training and convergence speeds than the TFT and Transformer, making it more efficient. Furthermore, our experiments on the TFT model demonstrate that integrating the VIX index can enhance the accuracy of exchange rate predictions.