Retrieval-Augmented Generation with Covariate Time Series
AI 摘要
针对时序预测，提出了一种无需训练、基于 regime 感知的 RAG 框架 RAG4CTS，并成功应用于工业场景。
主要贡献
- 提出了一种 regime 感知的时序 RAG 框架 RAG4CTS
- 构建了分层时间序列原生知识库，实现无损存储和物理信息检索
- 设计了两阶段双加权检索机制，对齐历史趋势
- 引入 Agent 驱动策略，以自监督方式动态优化上下文
方法论
构建分层时序知识库，通过双加权检索和 agent 驱动优化，实现 regime 感知的时序 RAG。
原文摘要
While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified in the Predictive Maintenance of the Pressure Regulating and Shut-Off Valve (PRSOV), a high-stakes industrial scenario characterized by (1) data scarcity, (2) short transient sequences, and (3) covariate coupled dynamics. Unfortunately, existing time-series RAG approaches predominantly rely on generated static vector embeddings and learnable context augmenters, which may fail to distinguish similar regimes in such scarce, transient, and covariate coupled scenarios. To address these limitations, we propose RAG4CTS, a regime-aware, training-free RAG framework for Covariate Time-Series. Specifically, we construct a hierarchal time-series native knowledge base to enable lossless storage and physics-informed retrieval of raw historical regimes. We design a two-stage bi-weighted retrieval mechanism that aligns historical trends through point-wise and multivariate similarities. For context augmentation, we introduce an agent-driven strategy to dynamically optimize context in a self-supervised manner. Extensive experiments on PRSOV demonstrate that our framework significantly outperforms state-of-the-art baselines in prediction accuracy. The proposed system is deployed in Apache IoTDB within China Southern Airlines. Since deployment, our method has successfully identified one PRSOV fault in two months with zero false alarm.