Optimization of Mathematical Models in Predictive Analysis for Economic Growth
الكلمات المفتاحية:
Economic Growth, Predictive Analysis, Mathematical Models, Model Optimization, Time-Series Analysis, Nonlinear Models, Gradient Descent, Lagrange Multipliers, Genetic Algorithms, Economic Forecastingالملخص
This research study examines the optimization of mathematical models in predictive analysis for economic growth. Accurate predictions of economic indices such as GDP, inflation, and employment rates are essential for informed policy creation and decision-making. This work analyzes contemporary mathematical models specifically linear, time-series, and nonlinear models employed in economic forecasting and highlights the optimization tactics implemented to enhance the prediction accuracy of these models. The research demonstrates the application of optimization methods such as gradient descent, Lagrange multipliers, and evolutionary algorithms to enhance model efficacy utilizing historical economic data. The results indicate that enhanced models provide superior predictive capabilities, facilitating policymaking and providing profound insights into economic trends. The results enhance the application of predictive analysis in economic planning and provide potential avenues for further research.
