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.

通过整合文本线索与时间序列数据，本研究引入了一种新颖的文本引导时间序列预测（TGTSF）任务，并提出了一个稳健的基准模型 TGForecaster，它利用交叉注意机制融合文本线索和时间序列数据。通过四个精心策划的基准数据集对所提出的框架进行了综合评估，验证了 TGForecaster 在各类数据中始终保持领先的性能，凸显了将文本信息纳入时间序列预测的潜力。该研究不仅开创了一种新的预测任务，而且为未来的研究建立了一个新的基准，推动了多模态数据集成在时间序列模型中的发展。