TAC-Time: Texts as Channels For Multimodal Time Series Forecasting

2026-09-21

AI Summary

TAC-Time通过将文本信息转换为时间通道，并与数值序列共同建模，解决了现有时间序列预测方法忽略文本信息的问题，提高了预测性能。

Abstract

Most existing time series forecasting methods rely solely on numerical observations, overlooking rich contextual information from auxiliary texts. Recent multimodal approaches attempt to incorporate textual signals, but they often treat text as static features or use large language models as forecasting backbones, limiting their ability to capture temporal dynamics and increasing computational cost. To address these challenges, we propose TAC-Time, a unified framework that transforms textual information into additional temporal channels. By modeling text features jointly with numerical sequences in a shared temporal backbone, TAC-Time preserves temporal continuity and periodic structures while remaining efficient and scalable. This formulation also enables systematic interpretability analyses. We show strong cross-modal dependencies through attention and frequency-domain analyses, and identify predictive textual signals whose correlation-aware alignment yields partial forecasting improvements. Extensive experiments on real-world multimodal benchmarks demonstrate that TAC-Time outperforms prior methods.

Problem

• time series forecasting
• multimodal
• textual information
• temporal dynamics
• computational cost

Innovation

• multimodal time series forecasting
• temporal channels
• shared temporal backbone
• cross-modal dependencies
• systematic interpretability