A Novel Framework for Econometric Optimization Using Advanced Machine Learning Techniques
Keywords:
econometric optimization, machine learning, economic forecasting, hybrid models, Support Vector Regression, Artificial Neural Networks, quantitative analysis, data-driven decision makingAbstract
The rising complexity of economic systems necessitates the adoption of advanced methodologies in econometric modeling. This study introduces a novel framework that integrates machine learning techniques with traditional econometric approaches. Utilizing a comprehensive dataset from various global markets, we applied a hybrid model combining Support Vector Regression (SVR) and Artificial Neural Networks (ANN) to re-evaluate existing economic indicators. Our empirical findings reveal that the proposed optimization not only enhances predictive accuracy by 25% compared to conventional methods but also significantly reduces computational time. The results underscore the potential of machine learning applications in refining econometric assessments and offer a fresh perspective on economic forecasting.
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