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、气象及物联网应用中具有重要作用。近期研究借助大语言模型的泛化能力适配时间序列预测任务，取得了可观性能。然而，现有研究聚焦于token级模态对齐，而非弥合语言知识结构与时间序列数据模式间的固有模态鸿沟，这极大限制了语义表征能力。为解决该问题，我们提出新型语义增强大语言模型（SE-LLM），通过挖掘时间序列固有的周期性与异常特征，将其嵌入语义空间以增强token嵌入。该过程提升了LLM对token的可解释性，从而激活其在时序分析中的潜能。此外，现有基于Transformer的LLM擅长捕获长程依赖关系，但在建模时间序列中短期异常方面存在不足。为此，我们提出一种嵌入自注意力机制的插件模块，通过建模长短期依赖关系使LLM有效适配时间序列分析。本方法冻结LLM参数并降低token序列维度，大幅减少计算消耗。实验表明，所提SE-LLM方法相较于现有最先进方法表现出卓越性能。