TAC-Time：将文本作为多模态时间序列预测的通道
TAC-Time: Texts as Channels For Multimodal Time Series Forecasting
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中文总结 AI 辅助
TAC-Time提出将文本转化为时间通道，与数值序列联合建模，以捕捉跨模态动态，提升多模态时间序列预测性能。
中文摘要 AI 辅助
现有的大多数时间序列预测方法仅依赖数值观测，忽视了辅助文本中丰富的上下文信息。最近的多模态方法试图纳入文本信号，但它们往往将文本视为静态特征，或使用大型语言模型作为预测主干，这限制了它们捕捉时间动态的能力，并增加了计算成本。为解决这些挑战，我们提出了TAC-Time，一个将文本信息转化为额外时间通道的统一框架。通过在共享的时间主干中联合建模文本特征与数值序列，TAC-Time保持了时间连续性和周期性结构，同时保持高效和可扩展性。该公式还支持系统的可解释性分析。我们通过注意力和频域分析展示了强跨模态依赖，并识别出预测性文本信号，其相关性感知对齐带来了部分预测改进。在真实世界多模态基准上的大量实验表明，TAC-Time优于先前方法。
英文摘要
Most existing time series forecasting methods rely solely on numerical observations, overlooking rich contextual information from auxiliary texts. Recent multimodal approaches attempt to incorporate textual signals, but they often treat text as static features or use large language models as forecasting backbones, limiting their ability to capture temporal dynamics and increasing computational cost. To address these challenges, we propose TAC-Time, a unified framework that transforms textual information into additional temporal channels. By modeling text features jointly with numerical sequences in a shared temporal backbone, TAC-Time preserves temporal continuity and periodic structures while remaining efficient and scalable. This formulation also enables systematic interpretability analyses. We show strong cross-modal dependencies through attention and frequency-domain analyses, and identify predictive textual signals whose correlation-aware alignment yields partial forecasting improvements. Extensive experiments on real-world multimodal benchmarks demonstrate that TAC-Time outperforms prior methods.
发表机构
- East China Normal University（华东师范大学）
机构由 AI 辅助整理，请以论文原文为准。
