LLM让时间序列分析转向问答式理解，突破传统任务局限。
From Time Series Analysis to Question Answering: A Survey in the LLM Era
- 提出从时间序列分析到问答的演进框架，聚焦内外对齐机制。
- 归纳三类对齐范式，支持灵活、低成本、通用的模型选择。
- 适合关注时序智能、多模态问答的研究者与开发者。
近年来，大语言模型（LLMs）为时间序列分析（TSA）引入新范式，利用强大的语言能力支持预测与异常检测等任务。然而，这些任务难以覆盖时间序列的语言理解任务，如解释与描述。当前TSA与LLMs之间存在根本性差距：LLMs预训练目标是自然语言相关性，而非针对时间序列优化。为此，TSA正向时间序列问答（TSQA）演进，从专家驱动、任务特定转向用户驱动、任务统一的问答模式。TSQA依赖灵活探索，而非固定分析流程。本文首先提出一个反映该演进的分类体系，基于外部对齐到内部对齐的转变；随后将现有研究归纳为三类对齐范式：注入式对齐、桥接式对齐与内部对齐，并提供灵活、经济、可泛化的范式选择指导。最后，分析跨领域数据集特征，识别挑战并展望未来方向。
原文摘要 · Abstract (English)
Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities to support tasks such as forecasting and anomaly detection. However, these analysis tasks cannot adequately cover temporal language tasks, such as interpretation and captioning. A fundamental gap remains between TSA and LLMs: LLMs are pre-trained to optimize natural language relevance for question answering rather than objectives specialized for TSA. To bridge this gap, TSA is evolving toward Time Series Question Answering (TSQA), shifting from expert-driven and task-specific analysis to user-driven and task-unified question answering. TSQA depends on flexible exploration rather than predefined TSA pipelines. In this survey, we first propose a taxonomy that reflects the evolution from TSA to TSQA, driven by a shift from external to internal alignment. We then organize existing literature into three alignment paradigms: Injective Alignment, Bridging Alignment, and Internal Alignment, and provide practical guidance for flexible, economical, and generalizable selection of alignment paradigms. We finally analyze datasets across domains and characteristics, identify challenges, and highlight future research directions.
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