将时间序列结构与语言模型语义结合，提升多变量预测通用性。
SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs
- 分四阶段构建结构编码器，将时序数据映射到语言模型可理解的语义空间。
- 在多个数据集上优于强基线，显著改善跨任务泛化能力。
- 适合需要融合数值模式与语义推理的复杂时间序列预测场景。
多变量时间序列预测要求模型同时捕捉变量间的结构依赖并实现跨任务泛化。尽管结构编码器能有效建模特征交互，但缺乏支持语义级推理或任务自适应的能力。相反，大语言模型（LLMs）具备强大泛化能力，却难以直接处理原始时间序列输入。这一差距限制了统一、可迁移预测系统的发展。为此，我们提出SEED——一种嵌入驱动解码的结构编码器，包含四个阶段：感知令牌的编码器用于补丁提取，投影模块将补丁对齐至语言模型嵌入空间，语义重编程机制将补丁映射为任务感知原型，以及冻结的语言模型用于预测。该模块化架构将表征学习与推理分离，实现数值模式与语义推理的高效对齐。实验结果表明，所提方法在多个基准上持续优于强基线，不同数据集上的对比研究验证了SEED在弥合结构-语义建模鸿沟中的作用。
原文摘要 · Abstract (English)
Multivariate time series forecasting requires models to simultaneously capture variable-wise structural dependencies and generalize across diverse tasks. While structural encoders are effective in modeling feature interactions, they lack the capacity to support semantic-level reasoning or task adaptation. Conversely, large language models (LLMs) possess strong generalization capabilities but remain incompatible with raw time series inputs. This gap limits the development of unified, transferable prediction systems. Therefore, we introduce SEED, a structural encoder for embedding-driven decoding, which integrates four stages: a token-aware encoder for patch extraction, a projection module that aligns patches with language model embeddings, a semantic reprogramming mechanism that maps patches to task-aware prototypes, and a frozen language model for prediction. This modular architecture decouples representation learning from inference, enabling efficient alignment between numerical patterns and semantic reasoning. Empirical results demonstrate that the proposed method achieves consistent improvements over strong baselines, and comparative studies on various datasets confirm SEED's role in addressing the structural-semantic modeling gap.
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