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：多变量时间序列（MTS）常受缺失值、异常值和约束违反等并发质量问题的影响，严重损害下游分析。现有清洗方法仅能修复此类问题的有限子集，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签数据或领域特定规则，而在实际应用中这两种资源均极少可得。本文提出AegisTS——一种基于强化学习的智能体系统，专为清洗MTS中多种数据质量问题设计。我们将清洗过程构建为联合优化问题，同步处理质量问题顺序与清洗模型选择，从而在庞大的清洗管线空间中实现高效导航。该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，低层智能体则为每个问题选取最适配的清洗方法。为引导智能体找到最优清洗管线，我们提出一种双重奖励机制，将上游（清洗）与下游性能耦合，无需依赖真实标签即可实现有效优化。实验结果表明，AegisTS在数据清洗质量上最高提升96%，下游性能提升27%，持续优于现有方法。