Time series forecasting plays a significant role in finance, energy, meteorology, and IoT applications. Recent studies have leveraged the generalization capabilities of large language models (LLMs) to adapt to time series forecasting, achieving promising performance. However, existing studies focus on token-level modal alignment, instead of bridging the intrinsic modality gap between linguistic knowledge structures and time series data patterns, greatly limiting the semantic representation. To address this issue, we propose a novel Semantic-Enhanced LLM (SE-LLM) that explores the inherent periodicity and anomalous characteristics of time series to embed into the semantic space to enhance the token embedding. This process enhances the interpretability of tokens for LLMs, thereby activating the potential of LLMs for temporal sequence analysis. Moreover, existing Transformer-based LLMs excel at capturing long-range dependencies but are weak at modeling short-term anomalies in time-series data. Hence, we propose a plugin module embedded within self-attention that models long-term and short-term dependencies to effectively adapt LLMs to time-series analysis. Our approach freezes the LLM and reduces the sequence dimensionality of tokens, greatly reducing computational consumption. Experiments demonstrate the superiority performance of our SE-LLM against the state-of-the-art (SOTA) methods.

翻译：时间序列预测在金融、能源、气象学及物联网应用中发挥着重要作用。近期研究利用大语言模型（LLMs）的泛化能力适配时间序列预测任务，并取得了有前景的性能表现。然而，现有研究聚焦于词元级别的模态对齐，而非弥合语言知识结构与时序数据模式之间的内在模态鸿沟，这严重限制了语义表征能力。为解决该问题，我们提出一种新颖的语义增强大语言模型（SE-LLM），通过挖掘时间序列的固有周期性与异常特征，将其嵌入语义空间以增强词元嵌入。该过程提升了LLMs对词元的可解释性，从而激活LLMs在时序序列分析中的潜力。此外，现有基于Transformer的LLMs擅长捕捉长程依赖关系，但对时序数据中的短期异常建模能力较弱。为此，我们提出一种内嵌于自注意力机制的插件模块，通过联合建模长程与短程依赖关系，使LLMs有效适配时序分析任务。本方法冻结LLM参数并降低词元序列维度，显著减少计算消耗。实验表明，我们的SE-LLM相较于现有最优方法（SOTA）展现了卓越性能。