While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information, remains in its infancy. With recent progress in large language models and time series learning, we revisit the integration of paired texts with time series through the Platonic Representation Hypothesis, which posits that representations of different modalities converge to shared spaces. In this context, we identify that time-series-paired texts may naturally exhibit periodic properties that closely mirror those of the original time series. Building on this insight, we propose a novel framework, Texts as Time Series (TaTS), which considers the time-series-paired texts to be auxiliary variables of the time series. TaTS can be plugged into any existing numerical-only time series models and effectively enable them to handle time series data with paired texts. Through extensive experiments on both multimodal time series forecasting and imputation tasks across benchmark datasets with various existing time series models, we demonstrate that TaTS can enhance multimodal predictive performance without modifying model architectures. Our Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/TaTS.

翻译：尽管时间序列模型的许多进展仅专注于数值数据，但涉及多模态时间序列（特别是包含上下文文本信息的研究）仍处于起步阶段。随着大语言模型和时间序列学习的最新进展，我们通过柏拉图式表征假说重新审视了配对文本与时间序列的融合，该假说认为不同模态的表征会收敛到共享空间。在此背景下，我们发现时序配对文本可能天然呈现出与原始时间序列高度相似的周期性特征。基于这一洞见，我们提出了一个新颖的框架——文本即时间序列（TaTS），该框架将时序配对文本视为时间序列的辅助变量。TaTS可无缝嵌入任何现有的纯数值时间序列模型，并有效使其能够处理带有配对文本的时间序列数据。通过在基准数据集上对多模态时间序列预测和填补任务进行大量实验，并结合多种现有时间序列模型，我们证明TaTS无需修改模型架构即可提升多模态预测性能。我们的代码发布于 https://github.com/iDEA-iSAIL-Lab-UIUC/TaTS。