Multivariate time series (MTS) are frequently affected by co-occurring quality issues, such as missing values, outliers, and constraint violations, which significantly undermine downstream analytics. Existing cleaning approaches fix only a limited set of such issues, making them ill-suited for scenarios where multiple quality problems arise simultaneously. Furthermore, these methods commonly depend on the availability of ground truth data or domain-specific rules, both of which are rarely accessible in real-world applications. In this paper, we introduce \sys, an agent system with reinforcement learning designed to clean multiple data quality issues in MTS. We cast the cleaning process as a joint optimization problem that simultaneously handles quality issue order and cleaning model selection, allowing efficient navigation of the large space of possible cleaning pipelines. Our framework relies on a hierarchical agent architecture, where a high-level agent determines the order in which data quality issues should be processed, while a low-level agent identifies the most suitable cleaning method for each issue. To guide the agent toward an optimal cleaning pipeline, we propose a dual-stage reward mechanism that couples upstream (cleaning) and downstream performance, enabling effective optimization without relying on ground truth. Our experimental results show that \sys consistently outperforms existing methods, achieving up to 96\% improvement in data cleaning quality and 27\% improvement in downstream performance.

翻译：多元时间序列常受缺失值、异常值和约束违规等并发质量问题的影响，这些缺陷显著损害下游分析性能。现有清洗方法仅能修复有限类型的问题，难以应对多种质量问题同时出现的场景。此外，这些方法通常依赖真实标注数据或领域特定规则，而在实际应用中两者均难以获取。本文提出\sys——一种基于强化学习的智能体系统，旨在解决多元时间序列中的多重数据质量问题。我们将清洗过程建模为联合优化问题，同步处理质量问题处理顺序与清洗模型选择，从而高效遍历可能的清洗流水线组合空间。框架采用分层智能体架构：高层智能体决定数据质量问题的处理顺序，底层智能体为每个问题选择最适清洗方法。为引导智能体生成最优清洗流水线，我们提出双阶段奖励机制，耦合上游（清洗）与下游性能指标，无需依赖真实标注即可实现有效优化。实验结果表明，\sys一致性地超越现有方法，在数据清洗质量上最高提升96%，下游性能提升27%。