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 AegisTS, 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 AegisTS 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（MTS）常受缺失值、异常值和约束违反等并发质量问题的困扰，这些问题严重损害下游分析。现有清洗方法仅能解决有限类型的质量问题，难以应对多种质量问题同时出现的场景。此外，这些方法普遍依赖真实标注数据或领域特定规则，而两者在现实应用中往往难以获取。本文提出AegisTS——一种基于强化学习的智能体系统，专用于解决MTS中的多重数据质量问题。我们将清洗过程建模为同时处理质量问题排序和清洗模型选择的联合优化问题，从而高效遍历可能的清洗流水线空间。本框架采用分层智能体架构：高层智能体决定质量问题处理顺序，低层智能体为每个问题选择最合适的清洗方法。为引导智能体找到最优清洗流水线，我们设计了一种双阶段奖励机制，将上游（清洗）性能与下游性能耦合，在不依赖真实标注数据的情况下实现有效优化。实验结果表明，AegisTS始终优于现有方法，在数据清洗质量上提升高达96%，下游性能提升达27%。