Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

arXiv:2606.14941v1 公告类型：新 摘要：时间序列预测模型通常受益于历史模式。受检索增强生成（RAG）的启发，最近的研究探索了检索相关的历史时间序列片段以增强预测。然而，仅仅依靠时间序列相似性往往不足以进行非平稳性下的检索。 To address this, we propose a multimodal approach: a \textbf{S}emantics-\textbf{E}nhanced \textbf{R}etrieval-\textbf{A}ugmented Time Series \textbf{F}orecasting framework, SERAF. Unlike mainstream approaches that depend only on time series similarity, SERAF conducts dual retrieval over the time series and their self-generated textual descriptions. It retrieves two complementary sets of historical patterns and corresponding futures, which are selectively and jointly used to guide future predictions. Experiments across seven real-world datasets demonstrate the effectiveness of SERAF in bridging numerical and semantic views of time series compared with state-of-the-art baselines.