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

Understand

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.