两次回顾：面向时间序列推理的互补表示
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
AI总结：
本文提出互补表示设计原则，构建基于Qwen3-VL-8B的双视图时间序列模型，在TSRBench等基准上大幅提升时间序列推理性能，验证互补表示的有效性。
摘要：
时间序列语言模型（TSLMs）支持对时序数据进行基于语言的理解与推理，但核心问题仍待解决：其时间序列表示应保留哪些信息，又应排除哪些变化？现有TSLMs通常仅向语言模型提供单一表示，为下游任务施加固定归纳偏置。本文提出互补表示作为时间序列推理的设计原则，模型结合两种视觉视图：折线图保留绝对测量值与观测坐标系，归一化延迟嵌入则排除正振幅缩放与偏移，同时呈现时序结构。专用视觉编码器通过视觉令牌与多级特征融合，将两种视图整合至共享语言模型。评估阶段干预验证两种视图具有不同不变性特性，单视图消融实验显示，二者组合在基础感知之外的预测与推理任务中带来显著增益。基于Qwen3-VL-8B构建的双视图模型，将TSRBench整体准确率从仅用折线图的44.5%提升至53.2%，远超评估中最强专用TSLM基线的36.7%；在TSExam上准确率达89.9%，在多变量ChatTS对齐任务上数值准确率为86.8%。这些结果表明，互补表示是通用TSLMs的有效设计原则，可使模型在不牺牲原始测量坐标系访问权限的前提下利用时序结构。
英文摘要：
Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what information should their time-series representations preserve, and what variation should they factor out? Existing TSLMs typically expose the language model to a single representation, imposing a fixed inductive bias across downstream tasks. We instead introduce complementary representations as a design principle for time-series reasoning. Our model combines two visual views: line plots preserve absolute measurements and the observed coordinate frame, while normalized delay embeddings factor out positive amplitude rescaling and offsets while exposing temporal structure. Dedicated visual encoders integrate both views into a shared language model through visual tokens and multi-level feature fusion. Evaluation-time interventions verify that the two views exhibit distinct invariance profiles, while single-view ablations show that their combination is particularly beneficial beyond basic perception, with the large gains in prediction and reasoning. Built on Qwen3-VL-8B, our dual-view model improves overall TSRBench accuracy from 44.5% with line plots alone to 53.2%, substantially exceeding the strongest evaluated dedicated TSLM baseline at 36.7%. It further achieves 89.9% accuracy on TSExam and 86.8% numerical accuracy on multivariate ChatTS alignment. Together, these results demonstrate that complementary representations provide an effective design principle for general-purpose TSLMs, enabling models to exploit temporal structure without sacrificing access to the original measurement frame.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
