Dual Neural Paradigm: GRU-LSTM Hybrid for Precision Exchange Rate Predictions

The USD/RMB exchange rate is significant when examining the structure of the Chinese financial system. Predicting the accurate USD/RMB exchange rate enables individuals to analyze the condition of the economy and prevent losses. We propose a novel hybrid approach of GRU-LSTM to improve the forecast of the future USD/RMB exchange rate. Deep learning techniques have become the cornerstone of numerous computer vision and natural language processing fields. This paper discusses various aspects and aims to show that they can help predict the exchange rate. We investigate how the newly developed hybrid GRU-LSTM model performs in terms of success rate and profitability compared with the LSTM and GRU models. The evaluation of the model is done on the USD/RMB currency pair and the forecasts made from September 13, 2023, to December 11, 2023. To increase the accuracy of the model, metrics like mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute relative error (MAPE) were introduced. The study found that the novel hybrid GRU-LSTM model was performing relatively well compared to the models of LSTM and GRU deployed in the survey for exchange rate prediction. This improvement can significantly benefit the analyst or trader in making the right decisions on the management of risks. The study further opens new possibilities for using the hybrid GRU-LSTM model by demonstrating the enhanced potential of this method, which can be more effective in the financial environment. Subsequent studies might improve the forecast by increasing the set of hybrid models and including more economic variables.

@article{Butt2025,
  title     = {Dual Neural Paradigm: GRU-LSTM Hybrid for Precision Exchange Rate Predictions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shamaila Butt},
  doi       = {10.14569/IJACSA.2025.01604100},
  url       = {https://doi.org/10.14569/IJACSA.2025.01604100}
}