Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative

While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information commonly encountered in real-world scenarios, remains in its infancy. Consequently, effectively integrating the text modality remains challenging. In this work, we highlight an intuitive yet significant observation that has been overlooked by existing works: time-series-paired texts 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 enable them to handle time series data with paired texts effectively. 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 predictive performance and achieve outperformance without modifying model architectures.