ELM-GA-ERWCA: A Tailored Forex Exchange Trading Model Combining Extreme Learning Machine with Genetic Algorithm and Evaporation Based Water Cycle Algorithm

Abstract

Accurate forecasting of foreign exchange (Forex) rates is critical for financial decision-making, given the nonlinear and stochastic nature of market data. This study proposes a hybrid forecasting model, ELM-GA-ERWCA, which integrates extreme learning machine (ELM) with genetic algorithm (GA) and evaporation rate water cycle algorithm (ERWCA). Three currency pairs, USD:INR, SAR:INR, and SGD:INR, were analyzed using 4000 daily samples (2005–2020). Datasets were reconstructed with technical indicators and evaluated in both segregated and un-segregated forms. Performance was assessed using RMSE, MAPE, and R2 across short- and long-term prediction horizons. Results show that the proposed model consistently outperforms baseline models (ELM-GA, ELM-WCA, ELM-ERWCA), achieving RMSE reductions of up to 12%, MAPE improvements of 8–10%, and R2 values above 0.99. Convergence analysis confirmed faster and more stable optimization, while Friedman statistical validation established the robustness of the approach. The findings demonstrate that ELM-GA-ERWCA provides a statistically reliable framework for Forex prediction, with potential for future integration into trading simulations and risk-aware financial applications. The proposed ELM-GA-ERWCA model demonstrates statistically robust forecasting accuracy across multiple currency datasets. Its lower error margins and consistent convergence behavior indicate potential for practical application in financial decision support systems. However, its economic implications must be further validated through trading simulations and backtesting frameworks before being considered a risk-minimizing tool for investors. Figure A gives complete idea about the summary of the work.