基于对比学习的动态图表示在金融市场预测中的应用：融合时间演化与静态关系
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
AI总结：
提出DGRCL框架，通过EE模块捕捉股票时间演化并利用CCT模块施加静态关系约束，结合对比学习显著提升了NASDAQ和NYSE数据集上的股票趋势预测准确率。
摘要：
时间图学习（TGL）对于捕捉股票市场的演化特性至关重要。传统方法往往忽略了动态时间变化与股票间静态关系结构之间的相互作用。为了解决这个问题，我们提出了基于对比学习的动态图表示（DGRCL）框架，该框架整合了动态和静态图关系，以提高股票趋势预测的准确性。我们的框架引入了两个关键组件：嵌入增强（EE）模块和对比约束训练（CCT）模块。EE模块专注于动态捕捉股票数据的时间演化，而CCT模块则基于股票关系施加静态约束，并在对比学习中加以细化。这种双重关系方法能够更全面地理解股票市场的动态。我们在美国两大主要股票市场数据集NASDAQ和NYSE上的实验表明，DGRCL显著优于最先进的TGL基线。消融研究表明这两个模块的重要性。总体而言，DGRCL不仅增强了预测能力，还为在动态图中整合时间数据和关系数据提供了一个稳健的框架。代码和数据已公开提供。
英文摘要：
Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. 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. Our framework introduces two key components: the Embedding Enhancement (EE) module and the Contrastive Constrained Training (CCT) module. The EE module focuses on dynamically capturing the temporal evolution of stock data, while the CCT module enforces static constraints based on stock relations, refined within contrastive learning. This dual-relation approach allows for a more comprehensive understanding of stock market dynamics. Our experiments on two major U.S. stock market datasets, NASDAQ and NYSE, demonstrate that DGRCL significantly outperforms state-of-the-art TGL baselines. Ablation studies indicate the importance of both modules. Overall, DGRCL not only enhances prediction ability but also provides a robust framework for integrating temporal and relational data in dynamic graphs. Code and data are available for public access.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
