Outlier detection and concept drift detection represent two challenges in data analysis. Most studies address these issues separately. However, joint detection mechanisms in regression remain underexplored, where the continuous nature of output spaces makes distinguishing drifts from outliers inherently challenging. To address this, we propose a novel robust regression framework for joint outlier and concept drift detection. Specifically, we introduce a dual-channel decision process that orchestrates prediction residuals into two coupled logic flows: a rapid response channel for filtering point outliers and a deep analysis channel for diagnosing drifts. We further develop the Exponentially Weighted Moving Absolute Deviation with Distinguishable Types (EWMAD-DT) detector to autonomously differentiate between abrupt and incremental drifts via dynamic thresholding. Comprehensive experiments on both synthetic and real-world datasets demonstrate that our unified framework, enhanced by EWMAD-DT, exhibits superior detection performance even when point outliers and concept drifts coexist.

翻译：离群点检测与概念漂移检测是数据分析中的两大挑战。多数研究分别处理这两个问题，然而回归任务中的联合检测机制仍待深入探索，其中输出空间的连续性使得区分漂移与离群点具有本质困难。为此，我们提出一种新颖的鲁棒回归框架，用于联合检测离群点与概念漂移。具体而言，我们设计了一种双通道决策流程，将预测残差组织为两个耦合的逻辑流：快速响应通道用于过滤点状离群值，深度分析通道用于诊断漂移。我们进一步开发了可区分类型的指数加权移动绝对偏差检测器，通过动态阈值技术自主区分突变漂移与渐进漂移。在合成数据集与真实数据集上的综合实验表明，经EWMAD-DT增强的统一框架即使在点状离群值与概念漂移共存时，仍展现出卓越的检测性能。