将时间序列推理能力压缩到小型语言模型中，实现可解释的时序分析。
Towards Interpretable Time Series Foundation Models
- 用合成数据生成自然语言标注，指导小模型指令微调。
- 模型能准确识别趋势方向、噪声强度和极值位置。
- 适合对隐私敏感或需本地部署的轻量级时序解释场景。
本文探索将时间序列推理能力蒸馏至小型指令微调语言模型中，以构建可解释的时间序列基础模型。基于具有系统性变化趋势与噪声水平的均值回归合成数据集，利用大模型生成自然语言注释，并以此监督紧凑型Qwen模型的微调。提出评估指标，重点考察趋势方向、噪声强度和极值定位的准确性，结果表明微调后模型具备有意义的解释能力。实验验证了将时间序列理解压缩为轻量化、具备语言能力模型的可行性，适用于设备端或隐私敏感场景部署。本工作为开发小型、可解释的时序模式解释模型提供了切实路径。
原文摘要 · Abstract (English)
In this paper, we investigate the distillation of time series reasoning capabilities into small, instruction-tuned language models as a step toward building interpretable time series foundation models. Leveraging a synthetic dataset of mean-reverting time series with systematically varied trends and noise levels, we generate natural language annotations using a large multimodal model and use these to supervise the fine-tuning of compact Qwen models. We introduce evaluation metrics that assess the quality of the distilled reasoning - focusing on trend direction, noise intensity, and extremum localization - and show that the post-trained models acquire meaningful interpretive capabilities. Our results highlight the feasibility of compressing time series understanding into lightweight, language-capable models suitable for on-device or privacy-sensitive deployment. This work contributes a concrete foundation toward developing small, interpretable models that explain temporal patterns in natural language.
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