Integrating generative ai into financial market prediction for improved decision making

HIGHLIGHTS

• What: This study provides an in-depth analysis of model architecture and key technologies of generative artificial intelligence combined with specific application cases and uses conditional generative adversarial networks ( cGAN ) and time series analysis methods to simulate and predict dynamic changes in financial markets. research results show that cGAN model can effectively capture complexity of financial market data and deviation between prediction results and actual market performance is minimal showing a high degree of accuracy. In this study, a comprehensive evaluation is conducted on the application effect of generative artificial_intelligence technology in financial market forecasting.
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