From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912966976208896 |
|---|---|
| author | Li, Lehui Wang, Yuyao Yan, Jisheng Zhang, Wei Deng, Jinliang Sun, Haoliang Han, Zhongyi Gong, Yongshun |
| author_facet | Li, Lehui Wang, Yuyao Yan, Jisheng Zhang, Wei Deng, Jinliang Sun, Haoliang Han, Zhongyi Gong, Yongshun |
| contents | Incorporating textual information into time-series forecasting holds promise for addressing event-driven non-stationarity; however, a fundamental modality gap hinders effective fusion: textual descriptions express temporal impacts implicitly and qualitatively, whereas forecasting models rely on explicit and quantitative signals. Through controlled semi-synthetic experiments, we show that existing methods over-attend to redundant tokens and struggle to reliably translate textual semantics into usable numerical cues. To bridge this gap, we propose TESS, which introduces a Temporal Evolution Semantic Space as an intermediate bottleneck between modalities. This space consists of interpretable, numerically grounded temporal primitives (mean shift, volatility, shape, and lag) extracted from text by an LLM via structured prompting and filtered through confidence-aware gating. Experiments on four real-world datasets demonstrate up to a 29 percent reduction in forecasting error compared to state-of-the-art unimodal and multimodal baselines. The code will be released after acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12664 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space Li, Lehui Wang, Yuyao Yan, Jisheng Zhang, Wei Deng, Jinliang Sun, Haoliang Han, Zhongyi Gong, Yongshun Computation and Language Artificial Intelligence Incorporating textual information into time-series forecasting holds promise for addressing event-driven non-stationarity; however, a fundamental modality gap hinders effective fusion: textual descriptions express temporal impacts implicitly and qualitatively, whereas forecasting models rely on explicit and quantitative signals. Through controlled semi-synthetic experiments, we show that existing methods over-attend to redundant tokens and struggle to reliably translate textual semantics into usable numerical cues. To bridge this gap, we propose TESS, which introduces a Temporal Evolution Semantic Space as an intermediate bottleneck between modalities. This space consists of interpretable, numerically grounded temporal primitives (mean shift, volatility, shape, and lag) extracted from text by an LLM via structured prompting and filtered through confidence-aware gating. Experiments on four real-world datasets demonstrate up to a 29 percent reduction in forecasting error compared to state-of-the-art unimodal and multimodal baselines. The code will be released after acceptance. |
| title | From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.12664 |