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、气象和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)的泛化能力来适应时间序列预测，并取得了令人瞩目的性能。然而，现有研究侧重于词元级别的模态对齐，而非弥合语言知识结构与时间序列数据模式之间的固有机理差异，这极大地限制了语义表征能力。为解决此问题，我们提出了一种新颖的语义增强大语言模型(SE-LLM)，该模型通过挖掘时间序列固有的周期性和异常特征，将其嵌入语义空间以增强词元嵌入。该过程增强了词元对大语言模型的可解释性，从而激活大语言模型在时序序列分析中的潜力。此外，现有的基于Transformer的大语言模型擅长捕获长程依赖关系，但在建模时间序列数据中的短期异常方面表现不足。因此，我们提出一个嵌入自注意力机制的插件模块，用于建模长程与短程依赖关系，从而有效使大语言模型适配时间序列分析。我们的方法冻结大语言模型并降低词元的序列维度，大幅减少了计算消耗。实验表明，我们提出的SE-LLM在性能上优于现有最先进(SOTA)方法。