Time series (TS) data are ubiquitous across various application areas, rendering time series forecasting (TSF) a fundamental task. With the astounding advances in large language models (LLMs), a variety of methods have been developed to adapt LLMs for time series forecasting. Despite unlocking the potential of LLMs in comprehending TS data, existing methods are inherently constrained by their shallow integration of TS information, wherein LLMs typically access TS representations at shallow layers, primarily at the input layer. This causes the influence of TS representations to progressively fade in deeper layers and eventually leads to ineffective adaptation between textual embeddings and TS representations. In this paper, we propose the Multi-layer Steerable Embedding Fusion (MSEF), a novel framework that enables LLMs to directly access time series patterns at all depths, thereby mitigating the progressive loss of TS information in deeper layers. Specifically, MSEF leverages off-the-shelf time series foundation models to extract semantically rich embeddings, which are fused with intermediate text representations across LLM layers via layer-specific steering vectors. These steering vectors are designed to continuously optimize the alignment between time series and textual modalities and facilitate a layer-specific adaptation mechanism that ensures efficient few-shot learning capabilities. Experimental results on seven benchmarks demonstrate significant performance improvements by MSEF compared with baselines, with an average reduction of 31.8% in terms of MSE. The code is available at https://github.com/One1sAll/MSEF.

翻译：时间序列（TS）数据在各应用领域中普遍存在，使得时间序列预测（TSF）成为一项基础任务。随着大语言模型（LLM）的惊人进展，多种方法被开发出来以适配LLM进行时间序列预测。尽管这些方法释放了LLM在理解TS数据方面的潜力，但现有方法本质上受限于其对TS信息的浅层整合，即LLM通常在浅层（主要是输入层）访问TS表示。这导致TS表示的影响在更深层逐渐减弱，最终造成文本嵌入与TS表示之间的适配失效。本文提出多层可调控嵌入融合（MSEF），一种新颖的框架，使LLM能够在所有深度直接访问时间序列模式，从而缓解TS信息在深层逐渐丢失的问题。具体而言，MSEF利用现成的时间序列基础模型提取语义丰富的嵌入，并通过层特定的调控向量将其与LLM各层的中间文本表示融合。这些调控向量旨在持续优化时间序列与文本模态之间的对齐，并促进层特定的适配机制，确保高效的小样本学习能力。在七个基准数据集上的实验结果表明，MSEF相较于基线方法实现了显著的性能提升，平均均方误差（MSE）降低了31.8%。代码可在https://github.com/One1sAll/MSEF获取。