This work introduces a novel Text-Guided Time Series Forecasting (TGTSF) task. By integrating textual cues, such as channel descriptions and dynamic news, TGTSF addresses the critical limitations of traditional methods that rely purely on historical data. To support this task, we propose TGForecaster, a robust baseline model that fuses textual cues and time series data using cross-attention mechanisms. We then present four meticulously curated benchmark datasets to validate the proposed framework, ranging from simple periodic data to complex, event-driven fluctuations. Our comprehensive evaluations demonstrate that TGForecaster consistently achieves state-of-the-art performance, highlighting the transformative potential of incorporating textual information into time series forecasting. This work not only pioneers a novel forecasting task but also establishes a new benchmark for future research, driving advancements in multimodal data integration for time series models.

翻译：本研究提出了一种新颖的文本引导时间序列预测任务。通过整合渠道描述与动态新闻等文本线索，该任务解决了传统方法单纯依赖历史数据的关键局限。为支持此任务，我们提出了TGForecaster——一个通过交叉注意力机制融合文本线索与时间序列数据的鲁棒基线模型。我们进而构建了四个精心设计的基准数据集以验证所提框架，数据范围涵盖简单周期性数据至复杂的事件驱动型波动。综合评估表明，TGForecaster始终能实现最先进的性能，彰显了将文本信息融入时间序列预测的变革潜力。本工作不仅开创了新颖的预测任务，更为未来研究确立了新基准，推动了时间序列模型多模态数据融合领域的发展。