迈向基于表示的通用过程控制

中文摘要 AI 辅助

许多时间过程学习与监控流程在局部窗口内运行，使得在实际操作中不可避免地需要做出窗口级决策。在此类设置下，经典统计检验可应用于单个窗口，但它们通常评估预定义的参数化假设（如单位根或基于矩的条件），从而在参考行为由任务或领域特定数据经验性地定义时限制了灵活性。在本工作中，我们将窗口级监控视为一个过程控制问题，并将其重新表述为基于参考的假设检验，其中原假设由经验参考分布而非固定参数模型指定。我们通过一个基于表示的非参数框架来实现这一视角，该框架结合了预训练时间序列编码器、核密度估计和共形校准，在学习到的表示空间中产生有限样本有效推断。平稳性和循环平稳性等经典概念在该框架内自然地作为经验参考集的实例出现。通过实验，我们展示了该方法对窗口级分布偏差的敏感性，同时在稳定参考机制下保持校准良好的推断，突出了所提方法对广泛时间序列过程控制与监控任务的适用性。

英文摘要

Many temporal process learning and monitoring pipelines operate in local windows, making window-level decisions unavoidable in practice. In such settings, classical statistical tests can be applied to individual windows, but they typically evaluate predefined parametric hypotheses-such as unit-root or moment-based conditions-thereby limiting flexibility when reference behavior is defined empirically from task- or domain-specific data. In this work, we view window-level monitoring as a process control problem and reformulate it as reference-based hypothesis testing, where the null hypothesis is specified by an empirical reference distribution rather than a fixed parametric model. We operationalize this perspective through a representation-based, nonparametric framework that combines pretrained time series encoders, kernel density estimation, and conformal calibration, yielding finite-sample valid inference in learned representation space. Classical notions such as stationarity and cyclostationarity arise as natural instantiations of empirical reference sets within this framework. Through experiments, we demonstrate sensitivity to window-level distributional deviations while maintaining well-calibrated inference under stable reference regimes, highlighting the applicability of the proposed approach to a broad class of time series process control and monitoring tasks.

发表机构

• Seoul National University（首尔大学）
• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。