用频域对齐文本与时间序列，提升预测准确性
Text2Freq: Learning Series Patterns from Text via Frequency Domain
- 将文本信息映射到时间序列的低频分量上实现跨模态对齐
- 在真实股市数据与合成文本上达到当前最优性能
- 适合关注多模态时间序列预测的研究者
传统时间序列预测模型主要依赖历史数值进行未来走势推断，但往往忽视了文本描述等其他模态中的丰富信息，而这些信息能为未来动态提供关键洞察。然而，与其它跨模态研究相比，文本与时间序列联合建模仍相对不足。此外，时间序列与文本之间的模态鸿沟也给多模态学习带来挑战。为此，我们提出Text2Freq，一种通过频域融合文本与时间序列数据的跨模态模型。具体而言，该方法将文本信息对齐至时间序列的低频成分，建立更有效且可解释的跨模态关联。在真实世界股价数据与合成文本组成的配对数据集上的实验表明，Text2Freq取得了当前最佳性能，其可扩展架构也为该领域后续研究提供了支持。
原文摘要 · Abstract (English)
Traditional time series forecasting models mainly rely on historical numeric values to predict future outcomes.While these models have shown promising results, they often overlook the rich information available in other modalities, such as textual descriptions of special events, which can provide crucial insights into future dynamics.However, research that jointly incorporates text in time series forecasting remains relatively underexplored compared to other cross-modality work. Additionally, the modality gap between time series data and textual information poses a challenge for multimodal learning. To address this task, we propose Text2Freq, a cross-modality model that integrates text and time series data via the frequency domain. Specifically, our approach aligns textual information to the low-frequency components of time series data, establishing more effective and interpretable alignments between these two modalities. Our experiments on paired datasets of real-world stock prices and synthetic texts show that Text2Freq achieves state-of-the-art performance, with its adaptable architecture encouraging future research in this field.
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