ARTIFICIAL INTELLIGENCE–DRIVEN ECONOMIC FORECASTING SYSTEMS: METHODOLOGICAL ADVANCES, EMPIRICAL EVIDENCE, AND POLICY IMPLICATIONS

The rapid development of artificial intelligence (AI) has fundamentally transformed economic forecasting by enabling more accurate, adaptive, and data-driven prediction systems. Traditional econometric models, while theoretically robust, often struggle to capture the nonlinear, dynamic, and high-dimensional nature of modern economic systems. This paper explores the conceptual foundations, methodological advancements, and empirical applications of AI-powered economic forecasting systems. Drawing on recent global evidence and macroeconomic data, the study highlights the advantages of machine learning and deep learning approaches in forecasting macroeconomic indicators, financial market trends, and structural economic changes. In addition, the article provides a focused analysis of emerging economies, emphasizing Uzbekistan’s digital transformation and macroeconomic performance. Empirical data demonstrate how AI-driven forecasting models contribute to improved policy formulation, financial stability, and long-term development planning. The findings confirm that AI-based forecasting systems significantly enhance predictive accuracy, responsiveness, and decision-making quality, while also raising important challenges related to transparency, data quality, and governance.人工智能（Artificial Intelligence，以下简称AI）的快速发展从根本上重塑了经济预测领域，催生了更精准、更具适应性且数据驱动的预测体系。传统计量经济学模型虽在理论层面具备严谨性，但往往难以捕捉现代经济系统的非线性、动态性与高维本质特征。本文探讨了AI驱动的经济预测系统的概念基础、方法学进展与实证应用。本研究依托近期全球实证证据与宏观经济数据，阐明了机器学习（Machine Learning）与深度学习（Deep Learning）方法在宏观经济指标、金融市场趋势及经济结构变化预测中的优势。此外，本文还针对新兴经济体展开专项分析，重点探讨乌兹别克斯坦的数字化转型与宏观经济运行表现。实证数据表明，AI驱动的预测模型可助力优化政策制定、维护金融稳定及完善长期发展规划。研究结果证实，基于AI的预测系统可显著提升预测精度、响应能力与决策质量，同时也带来了与透明度、数据质量及治理相关的诸多重要挑战。