关键词: 金融时间序列, 多模态, 混合专家模型, 预训练模型, 时频分析

Abstract: Abstract: Stock price prediction plays a critical role in financial markets, serving as a foundation for investment decision-making, risk management, asset allocation, and market evaluation. Stock price fluctuations not only directly impact investor returns but also reflect market responses to macroeconomic trends, policy shifts, and firm-level developments. Although deep learning has achieved notable success in time series forecasting, existing models are often limited to single-modality inputs, neglecting cross-industry correlations and modality heterogeneity, which hampers a comprehensive understanding of market dynamics. While some studies have introduced textual modalities, they typically fall short in addressing challenges such as modality asynchrony and multi-granularity alignment. To overcome these limitations, we propose MATCH, a multimodal fusion framework tailored for financial markets. MATCH integrates a mixture-of-experts pretraining strategy to mitigate modality lag and incorporate industry-specific knowledge. It further employs frequency-domain decomposition and hierarchical fusion mechanisms to enable effective cross-modal interaction under multi-granularity settings. Extensive experiments on real-world stock datasets demonstrate that MATCH significantly outperforms existing state-of-the-art methods in predictive performance.

Key words: financial time series, multimodal, mixture of experts, pretrained model, time-frequency analysis