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Found 2 papers, 1 papers with code
Cross-Modal Temporal Fusion for Financial Market Forecasting
Accurate financial market forecasting requires diverse data sources, including historical price trends, macroeconomic indicators, and financial news, each contributing unique predictive signals.
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction.
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