Synergistic advantages of deep learning and reinforcement learning in economic forecasting

Abstract

With the progress of science and technology, emerging technologies such as deep learning and reinforcement learning have emerged in economic forecasting, injecting synergy into this field. First of all, deep learning makes the economic model capture the dynamic changes of the economic system more comprehensively and accurately by dealing with nonlinear relations, learning complex characteristics and conducting accurate time series analysis. Its multi-level neural network structure and the ability to learn features automatically improve the adaptability of the model and avoid the tedious process of traditional manual feature selection. Secondly, the introduction of reinforcement learning gives the economic forecasting model more flexible and adaptive advantages. Through interactive learning between agent and environment, the model can optimize decision-making, deal with uncertainty better, and is adaptive. Reinforcement learning improves the flexibility of decision-making by constantly trying and learning and adjusting strategies to adapt to changes in the economic environment. The collaborative application of them has made remarkable progress in nonlinear relationship modeling, feature learning, time series analysis, decision making, uncertainty handling and self-adaptability, which has improved the accuracy and adaptability of the model and enhanced its robustness in the complex and changeable economic environment. In the future, deepening the research and optimization of these emerging technologies in practical application will provide better performance for economic forecasting, provide more comprehensive, accurate and practical information for decision makers, and help more scientific, flexible and fine economic management and policy formulation.