用语义空间把文本描述转化为可量化的预测信号，提升事件驱动型时间序列预测精度。
From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space
- 构建时序演化语义空间，将文本中的隐含时间影响转为数值化特征。
- 在四个真实数据集上，预测误差降低最高达29%。
- 适合需要融合新闻、报告等文本信息做预测的研究者。
将文本信息融入时间序列预测有望应对事件驱动的非平稳性问题；然而，语义模态间的根本差异阻碍了有效融合：文本描述以隐式、定性方式表达时间影响，而预测模型依赖显式、定量信号。通过受控的半合成实验，我们发现现有方法过度关注冗余词元，难以可靠地将文本语义转化为可用数值线索。为此，我们提出TESS，引入一个时序演化语义空间作为跨模态中间瓶颈。该空间包含由大语言模型通过结构化提示提取的可解释、数值化时序基元（均值偏移、波动率、形状、滞后），并经置信度感知门控过滤。在四个真实世界数据集上的实验表明，相比最先进单模态和多模态基线，预测误差最高降低29%。代码将在论文录用后发布。
原文摘要 · Abstract (English)
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.
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