用语义增强检索，提升非平稳时间序列预测精度
Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
- 双路检索：同时基于数值和自动生成的文本描述找历史模式
- 在7个真实数据集上优于当前最优基线模型
- 适合处理非平稳、复杂波动的时间序列场景
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）启发，近期研究尝试通过检索相关的历史时间序列片段来提升预测性能。然而，在非平稳条件下，仅依赖时间序列相似性往往效果不足。为此，我们提出一种多模态方法：语义增强的检索增强时间序列预测框架（SERAF）。不同于主流方法仅依赖时间序列相似性，SERAF在时间序列及其自动生成的文本描述上进行双重检索，获取两组互补的历史模式及对应未来片段，并选择性地联合使用以指导未来预测。在七个真实世界数据集上的实验表明，SERAF在融合数值与语义视角方面显著优于现有先进基线模型。
原文摘要 · Abstract (English)
Time series forecasting models often benefit from historical patterns. Inspired by Retrieval-Augmented Generation (RAG), recent research explored retrieving relevant historical time series segments to enhance forecasting. However, relying solely on time series similarity is often insufficient for retrieval under non-stationarity. 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.
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