Ensemble Hybrid LSTM Architectures for Robust Multi-Currency Forex Forecasting

The analysis of financial time series presents a longlasting obstacle regarding currency exchange rate forecasting because volatility and nonlinearity and non-stationarity characterize currency markets. The research presents an ensemble forecasting system which combines various deep learning and hybrid predictive models such as LSTM and GRU-LSTM and CNN-LSTM and Attention-LSTM and XGBoost-LSTM for scalable integration. The ensemble methodology follows a dynamic weighted averaging technique which bases its priority on assigning weights through the reciprocal calculation of Mean Squared Errors from individual models to identify accurate forecasters. A representative study based on the EUR/USD exchange rate took place as part of extensive evaluations that spanned various currency pairs. The standalone XGBoost-LSTM model proved most effective in terms of MSE and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values at 0.000088 and 0.9778 respectively. The ensemble model proved to be highly robust and generalizable through its outcomes which produced an MSE of 0.000142 along with MAE of 0.009204 and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9643. The ensemble approach stands as an effective and reliable method to increase both stability and predictive power of forex forecasting systems. The conceptual structure offers sound potential applications for algorithmic trading as well as financial risk management and multi-currency strategic decision-making systems.