TAC-Time: Texts as Channels For Multimodal Time Series Forecasting
TAC-Time is proposed, a unified framework that transforms textual information into additional temporal channels that preserves temporal continuity and periodic structures while remaining efficient and scalable and identifies predictive textual signals whose correlation-aware alignment yields partial forecasting improve...
Jiang-Yi Liang, Xiaotian Gu, Xin-Yue Xie et al.
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