LLaTiSA：从视觉感知到语义的难度分层时间序列推理

摘要

时间序列的全面理解对于大语言模型（LLMs）而言仍是一项重大挑战。当前研究受限于碎片化的任务定义和存在固有模糊性的基准测试，阻碍了严谨评估与统一时间序列推理模型（TSRMs）的发展。为弥补这一空白，我们通过构建包含四个认知复杂度递增层级的分类法，正式定义了时间序列推理（TSR）框架。我们推出HiTSR——一个包含8.3万个样本的层次化时间序列推理数据集，涵盖多样化的任务组合并附带经过验证的思维链（CoT）轨迹。基于HiTSR，我们提出LLaTiSA模型，该强时序推理模型通过将可视化模式与精度校准的数值表格相融合，显著增强了视觉语言模型（VLMs）的时间感知能力。采用多阶段课程精调策略后，LLaTiSA在各类TSR任务和现实场景中不仅实现了卓越性能，更展现出强大的分布外泛化能力。代码已开源：https://github.com/RainingNovember/LLaTiSA。

English

Comprehensive understanding of time series remains a significant challenge for Large Language Models (LLMs). Current research is hindered by fragmented task definitions and benchmarks with inherent ambiguities, precluding rigorous evaluation and the development of unified Time Series Reasoning Models(TSRMs). To bridge this gap, we formalize Time Series Reasoning (TSR) via a four-level taxonomy of increasing cognitive complexity. We introduce HiTSR, a hierarchical time series reasoning dataset comprising 83k samples with diverse task combinations and verified Chain-of-Thought (CoT) trajectories. Leveraging HiTSR, we propose LLaTiSA, a strong TSRM that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models (VLMs). Through a multi-stage curriculum fine-tuning strategy, LLaTiSA achieves superior performance and exhibits robust out-of-distribution generalization across diverse TSR tasks and real-world scenarios. Our code is available at https://github.com/RainingNovember/LLaTiSA.

LLaTiSA：从视觉感知到语义的难度分层时间序列推理

LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics

摘要

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