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arXiv · 2605.25943

STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy

Hui Cheng·Jinsheng Guo·Zhenhao Weng·Yan Qiao·Meng Li
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Abstract

Recent research in time series forecasting frequently investigates the integration of textual and visual modalities with numerical models to better navigate non-stationary environments. Despite delivering solid numerical results, existing multi-modal approaches usually encounter a dilemma: prioritizing the minimization of average errors can result in excessively smooth forecasts that overlook essential fluctuations. To resolve this limitation, we introduce STaT, an innovative multimodal architecture for Symbolic-Temporal-Textual Alignment, which seamlessly unites three synergistic modalities. Specifically, the symbolic modality converts continuous time series into discrete tokens, facilitating the accurate identification of structural patterns and turning points; the temporal modality extracts inherent sequential dependencies; and the textual modality leverages domain semantics to steer the macroscopic forecasting trends. Comprehensive evaluations on eight real-world benchmarks indicate that STaT delivers exceptional performance, enhancing conventional magnitude indicators by up to 8.9% while simultaneously decreasing shape distortion by up to 8.5%.

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Hui Cheng, Jinsheng Guo, Zhenhao Weng, Yan Qiao, Meng Li. 2026-05-25. STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy. https://arxiv.org/abs/2605.25943

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