Recently, leveraging pre-trained Large Language Models (LLMs) for time series (TS) tasks has gained increasing attention, which involves activating and enhancing LLMs' capabilities. Many methods aim to activate LLMs' capabilities based on token-level alignment, but overlook LLMs' inherent strength in natural language processing -- \textit{their deep understanding of linguistic logic and structure rather than superficial embedding processing.} We propose Context-Alignment (CA), a new paradigm that aligns TS with a linguistic component in the language environments familiar to LLMs to enable LLMs to contextualize and comprehend TS data, thereby activating their capabilities. Specifically, such context-level alignment comprises structural alignment and logical alignment, which is achieved by Dual-Scale Context-Alignment GNNs (DSCA-GNNs) applied to TS-language multimodal inputs. Structural alignment utilizes dual-scale nodes to describe hierarchical structure in TS-language, enabling LLMs to treat long TS data as a whole linguistic component while preserving intrinsic token features. Logical alignment uses directed edges to guide logical relationships, ensuring coherence in the contextual semantics. Following the DSCA-GNNs framework, we propose an instantiation method of CA, termed Few-Shot prompting Context-Alignment (FSCA), to enhance the capabilities of pre-trained LLMs in handling TS tasks. FSCA can be flexibly and repeatedly integrated into various layers of pre-trained LLMs to improve awareness of logic and structure, thereby enhancing performance. Extensive experiments show the effectiveness of FSCA and the importance of Context-Alignment across tasks, particularly in few-shot and zero-shot forecasting, confirming that Context-Alignment provides powerful prior knowledge on context. The code is open-sourced at https://github.com/tokaka22/ICLR25-FSCA.

翻译：近年来，利用预训练大型语言模型（LLMs）处理时间序列（TS）任务日益受到关注，其核心在于激活并增强LLMs的潜在能力。现有方法多基于词元级对齐来激活LLMs能力，却忽视了LLMs在自然语言处理中的固有优势——即其对语言逻辑与结构的深层理解，而非浅层的嵌入处理。本文提出上下文对齐（CA）这一新范式，通过将时间序列与LLMs熟悉的语言环境中的语言成分对齐，使LLMs能够将TS数据置于语境中理解，从而激活其能力。具体而言，这种上下文级对齐包含结构对齐与逻辑对齐，通过应用于TS-语言多模态输入的双尺度上下文对齐图神经网络（DSCA-GNNs）实现。结构对齐利用双尺度节点描述TS-语言中的层次结构，使LLMs能够将长序列TS数据视为整体语言成分，同时保留内在词元特征；逻辑对齐则通过有向边引导逻辑关系，确保上下文语义的连贯性。基于DSCA-GNNs框架，我们提出一种CA实例化方法——少样本提示上下文对齐（FSCA），以增强预训练LLMs处理TS任务的能力。FSCA可灵活、重复地集成至预训练LLMs的多个层级，提升其对逻辑与结构的感知，从而改进性能。大量实验验证了FSCA的有效性及上下文对齐在不同任务中的重要性，尤其在少样本与零样本预测任务中表现突出，证实了上下文对齐能为模型提供强大的上下文先验知识。代码已开源：https://github.com/tokaka22/ICLR25-FSCA。