Volume 5 , Issue 2 , PP: 01-29, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Safaa Zaman 1 * , El-Sayed M. El-Kenawy 2
This study addresses the critical challenge of financial forecasting for small businesses, which often struggle with fluctuating demand, seasonal sales patterns, and tight profit margins. Accurate forecasting is essential for optimizing resources, improving profitability, and making data-driven decisions in a dynamic market. To enhance the accuracy and efficiency of forecasting models, this paper introduces a novel approach combining machine learning models with metaheuristic optimization algorithms. Specifically, the Dynamic Attention Recurrent (DAR) model optimized with Logarithmic Transformation (LogTrans) is evaluated at various stages. In the baseline evaluation, the DAR + LogTrans model demonstrated outstanding performance with an MSE of 0.00075, RMSE of 0.0274, and R-squared of 0.861, indicating its strong predictive capability. After applying optimization techniques, DAR + LogTrans achieved remarkable improvements, reaching an MSE of 1.88E-07, RMSE of 4.36E-04, and R-squared of 0.968, showcasing substantial gains in accuracy and generalization. The results emphasize the potential of metaheuristic optimization, such as the Whale Optimization Algorithm (WAO), Bat Algorithm (BA), and Particle Swarm Optimization (PSO), in improving model performance. These findings provide valuable insights for small business owners seeking to implement advanced forecasting models that can adapt to market fluctuations. The optimized models, particularly DAR + LogTrans, offer a powerful tool for improving decision-making, managing cash flow, and enhancing operational efficiency, with significant implications for the future of financial forecasting in small businesses.
Financial Forecasting , Small Business Analytics , Revenue Prediction , Expense Management , Economic Decision-Making
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