DALG: A Dual Attention-Based LSTM-GRU Model for Exchange Rate Volatility Forecasting in China’s Forex Sector

Exchange rate volatility forecasting plays a vital role in guiding financial decisions and economic planning, particularly in China’s dynamic foreign exchange market. This study proposes a novel deep learning framework, termed DALG (Dual Attention-based LSTM-GRU), designed to capture complex temporal patterns and feature dependencies in high-frequency USD/RMB exchange rate data. By integrating LSTM and GRU architectures with a dual-stage attention mechanism, comprising input and temporal attention, the proposed DALG model enhances the interpretability and accuracy of exchange rate volatility forecasts. The model is empirically evaluated against benchmark models such as LSTM, GRU, and a hybrid LSTM-DA using standard performance metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demon-strate that the DALG model consistently outperforms traditional and hybrid deep learning models, offering superior predictive performance. The findings suggest that attention-enhanced deep learning architectures hold significant promise for robust financial time series modeling and forecasting in volatile forex markets.

@article{Butt2025,
  title     = {DALG: A Dual Attention-Based LSTM-GRU Model for Exchange Rate Volatility Forecasting in China’s Forex Sector},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shamaila Butt and Mohammad Abrar and Muhammad Ali Chohan and Muhammad Farrukh Shahzad},
  doi       = {10.14569/IJACSA.2025.0160886},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160886}
}