融合结构化与非结构化金融数据，提升股市走势预测准确率
Cross-Modal Temporal Fusion for Financial Market Forecasting
- 基于Transformer构建跨模态时间融合框架，对多源金融数据统一建模
- 在FTSE 100数据上实现优于传统与深度学习基线的涨跌方向分类效果
- 支持自动超参数调优，适合实际金融场景中复杂数据融合任务
金融市场精准预测需整合历史价格、宏观经济指标和财经新闻等多源数据。然而，现有模型常难以有效对齐不同模态，限制其实际应用。本文提出一种基于Transformer的深度学习框架——跨模态时间融合（CMTF），用于融合结构化与非结构化金融数据以提升市场预测能力。该模型包含张量解释模块用于特征选择，以及自动化训练流程实现高效超参数调优。基于FTSE 100股票数据的实验表明，CMTF在价格方向分类任务上显著优于经典方法与深度学习基线。结果表明，该框架是真实世界跨模态金融预测任务的有效且可扩展的解决方案。
原文摘要 · Abstract (English)
Accurate forecasting in financial markets requires integrating diverse data sources, from historical prices to macroeconomic indicators and financial news. However, existing models often fail to align these modalities effectively, limiting their practical use. In this paper, we introduce a transformer-based deep learning framework, Cross-Modal Temporal Fusion (CMTF), that fuses structured and unstructured financial data for improved market prediction. The model incorporates a tensor interpretation module for feature selection and an auto-training pipeline for efficient hyperparameter tuning. Experimental results using FTSE 100 stock data demonstrate that CMTF achieves superior performance in price direction classification compared to classical and deep learning baselines. These findings suggest that our framework is an effective and scalable solution for real-world cross-modal financial forecasting tasks.
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