A Novel Hybrid AI Approach for Enhancing Predictive Analytics in Financial Markets
Abstract
Financial market prediction remains a challenging problem due to volatility and complex dependencies. This paper proposes a novel hybrid AI approach that combines deep learning with symbolic AI and probabilistic modeling to enhance predictive analytics. We compare our approach against traditional statistical methods, deep neural networks, and ensemble learning techniques in forecasting stock prices, cryptocurrency trends, and economic indicators. The results demonstrate that our model achieves superior accuracy and robustness, outperforming conventional AI-driven financial models.
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