Hybrid Deep Learning Techniques Integrated with Machine Learning for Foreign Exchange Rate Forecasting

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to understand patterns and predict future trends. The creation of such prediction models can provide assistance for investors, financial institutions, and policymakers in governments. To overcome these issues, the proposed study has developed a novel hybrid deep learning model that encompasses a Bidirectional Long Short-Term Memory, an additive attention approach, and a random forest regressor (for long-horizon historical data), attempting to provide a prediction model for the following year’s official exchange rates (LCU per USD). The random forest regressor models the nonlinear interaction of features and assists with generalization, the attention layer focuses on the most influential time steps, and the Bidirectional Long Short-Term Memory (Bi-LSTM) captures all historical data for exchange rate series and temporal dependencies (or dependencies of a sequence of historical data). The use of a time partition (1960–