Enterprise AI Analysis
ELM-GA-ERWCA: A Tailored Forex Exchange Trading Model Combining Extreme Learning Machine with Genetic Algorithm and Evaporation Based Water Cycle Algorithm
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.
Executive Impact: Key Findings & Strategic Value
This analysis highlights the critical advancements and benefits of the ELM-GA-ERWCA model for enterprise-level financial forecasting.
- Novel Hybridization: Development of a new predictive model (ELM-GA-ERWCA) that combines the strengths of Genetic Algorithm and an evaporation-based Water Cycle Algorithm, addressing the limitations of convergence and local optima in earlier ELM-GA and ELM-WCA models.
- Dataset Segregation Strategy: Introduction of a unique data segregation mechanism for Forex time-series, which enhances learning efficiency and prediction accuracy compared with conventional unsegregated training.
- Comprehensive Forecasting Framework: Application of the model to three distinct Forex currency pairs (USD:INR, SAR:INR, SGD:INR) over both short- and long-term horizons, thereby establishing generalizability of the approach.
- Robust Scientific Validation: Performance benchmarking against established models using RMSE, MAPE, and R² measures, convergence analysis, and non-parametric statistical validation (Friedman test), which collectively demonstrate the statistical and methodological superiority of the proposed model.
RMSE Reduction
MAPE Improvement
R² Value
Faster Convergence
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Mostly, the international investments and trades are dealt with by converting the currency of one country to another, which is known as the Forex market. The process of purchasing one country's currency using another's for various transactions makes this financial market unique, and the important factors that make this industry more attractive include its continuous operations, a large number of trading activities, the spreading of business across geographical locations, and how this market influences more profit margins. It also has the potential for high gains and fast returns because a huge volume of currency is traded daily, and it provides opportunities for the liquidity of assets [1–5, 7–10]. The challenge of accurate prediction, as highlighted in studies [34–37], has been a key motivation for this research on financial market forecasting. This study aims to explore this area further and proposes an empirical forecasting model to predict the exchange rates of three currencies, with a particular focus on the Indian Rupee (INR).
This study proposed the ELM-GA-ERWCA framework as a tailored solution for Forex forecasting, addressing the challenges of non-linearity, stochasticity, and overfitting in time-series data. The model combines GA crossover with ERWCA optimization, achieving stable convergence and superior predictive performance across USD:INR, SAR:INR, and SGD:INR datasets. The detailed algorithm, parameter settings, and data preparation techniques, including segregation, are covered in this section.
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. 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. Comparative performance tables and convergence graphs are presented to support these findings.
While designing any forecasting model, the acceptance of this model is very crucial and important. The focus has to be given to perform the validation tests across the various and diverse types of forecasting models. Subjective assessments and empirical evaluation based on visual graphs and predicted plots, though widely used validation methods, statistical methods reveal most of the required information and attain the overall measure between observed and experimental predicted values [55–58]. In this study, the Friedman test [59–61] which is a non-parametric statistical test, has been used to validate the proposed and compared prediction models. Here H0 and H1 are considered as two hypotheses such as H0 represents all eight prediction models having the same probability distribution; and H1 defines that at least two of them differ from each other. The average rank of the algorithms of both un-segregated and segregated types of ELM-GA, ELM-WCA, ELM-ERWCA, and ELM-GA-ERWCA has been considered for this test and is shown in Table 13. The ranks obtained from this can be stated and analyzed as given below using Eq. (18).
0.99+
Achieved R² Value
ELM-GA-ERWCA Forex Forecasting Model Flow
Forex Dataset Collection
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Dataset Preparation
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Model Evaluation & Validation
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Exchange Price Prediction (Short and Long Term)
| Aspect | Prior Work | ELM-GA-ERWCA (This Study) |
|---|---|---|
| Hybrid Optimizer | ELM with GA or WCA in isolation, or other EAs | GA crossover with ERWCA evaporation/raining to optimize ELM's input-to-hidden weights, mitigating local optima while retaining fast ELM training. |
| Data Segregation | Conventional batching/windowing, or no segregation | Sequential, non-overlapping block strategy preserves temporal structure yet reduces computational load, improving convergence and stability across horizons. |
| Robustness Evidence | Limited to RMSE/MAPE, visual inspection | Rolling validation, convergence curves, non-parametric tests (Friedman; paired tests where applicable) demonstrate statistical meaningfulness and practical relevance. |
| Generalizability | Often limited to single currency pair | Consistent multi-horizon gains on USD:INR, SAR:INR, and SGD:INR indicate portability beyond a single currency pair. |
Impact on Forex Trading Decisions
The statistically robust forecasting accuracy of ELM-GA-ERWCA provides a reliable framework for Forex prediction. 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.
ROI: Enhanced prediction accuracy leading to informed trading decisions and potential risk reduction.
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