发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对少样本预测中语言直接生成时间序列难以保留时间结构的问题，提出以时间序列图为视觉中间步骤的VisualBridge方法，结合多模态大模型语义表示与预测奖励驱动的编辑策略，并利用时间VAE生成增强数据，在公开基准上优于传统增强方法。
AI 中文摘要
语义驱动的时间序列生成为少样本预测中的下游学习提供了一种有前景的改进方式，但直接从语言生成数值序列往往无法保留预期的时间结构。我们提出VisualBridge，利用时间序列图作为视觉中间步骤，以桥接高层时间语义与数值序列。一个多模态大语言模型（MLLM）首先将绘制的序列转换为结构化语义表示，从而能够对趋势、季节性和波动性等时间属性进行显式控制。随后，我们利用下游预测奖励学习一个语义编辑策略，使生成过程偏向于对目标任务有益的时间模式。生成的序列进一步通过时间变分自编码器（VAE）建模，以产生一致的多变量增强。在标准公开预测基准上的实验表明，VisualBridge在少样本预测中优于传统增强方法，消融实验验证了视觉语义锚定、学习到的语义控制以及基于VAE的生成各自的作用。
英文摘要
Semantic-driven time-series generation offers a promising way to improve downstream learning in few-shot forecasting, but directly generating numerical sequences from language often fails to preserve the intended temporal structure. We propose VisualBridge, which uses time-series plots as a visual intermediate to bridge high-level temporal semantics and numerical sequences. An MLLM first converts plotted series into structured semantic representations, enabling explicit control over temporal properties such as trend, seasonality, and volatility. We then learn a semantic editing policy with downstream forecasting rewards, allowing the generation process to favor temporal patterns that are beneficial for the target task. The resulting sequences are further modeled by a temporal VAE to produce consistent multivariate augmentations. Experiments on standard public forecasting benchmarks demonstrate that VisualBridge improves few-shot forecasting over conventional augmentation methods, with ablations validating the roles of visual semantic grounding, learned semantic control, and VAE-based generation.
