A Hybrid Metaheuristic Model for Efficient Analytical Business Prediction

Accurate and efficient business analytical predictions are essential for decision making in today's competitive landscape. Involves using data analysis, statistical methods, and predictive modeling to extract insights and make decisions. Current trends focus on applying business analytics to predictions. Optimizing business analytics predictions involves increasing the accuracy and efficiency of predictive models used to forecast future trends, behavior, and outcomes in the business environment. By analyzing data and developing optimization strategies, businesses can improve their operations, reduce costs, and increase profits. The analytic business optimization method uses a hybrid PSO (Particle Swarm Optimization) and GSO (Gravitational Search Optimization) algorithm to increase the efficiency and effectiveness of the decision-making process in business. In this approach, the PSO algorithm is used to explore the search space and find the global best solution, while the GSO algorithm is used to refine the search around the global best solution. The hybrid meta-heuristic method optimizes the three components of business analytics: descriptive, predictive, and perspective. The hybrid model is designed to strike a balance between exploration and exploitation, ensuring effective search and convergence to high-quality solutions. The results show that the R2 value for each optimization parameter is close to one, indicating a more fit model. The RMSE value measures the average prediction error, with a lower error indicating that the model is performing well. MSE represents the mean of the squared difference between the predicted and optimized values. A lower error value indicates a higher level of accuracy.