异常多元时间序列中的算法补救
Algorithmic Recourse in Abnormal Multivariate Time Series
AI总结：
本文提出RecAD框架，通过回溯反事实推理为多元时间序列异常提供算法补救，端到端训练补救函数，实验验证其有效性。
摘要：
算法补救提供可操作的建议，以改变机器学习模型的不利预测，通过反事实解释增强透明度。尽管在静态数据（如表格和图像数据）的算法补救方面已取得显著进展，但针对多元时间序列的补救研究有限，尤其是在逆转异常时间序列方面。本文介绍了时间序列异常检测中的补救（RecAD）框架，该框架利用回溯反事实推理来处理多元时间序列中的异常。通过将异常原因建模为对外生变量的外部干预，RecAD预测补救动作以恢复正常状态作为反事实解释，其中负责基于观测数据生成动作的补救函数采用端到端方法进行训练。在合成和真实世界数据集上的实验证明了其有效性。
英文摘要：
Algorithmic recourse provides actionable recommendations to alter unfavorable predictions of machine learning models, enhancing transparency through counterfactual explanations. While significant progress has been made in algorithmic recourse for static data, such as tabular and image data, limited research explores recourse for multivariate time series, particularly for reversing abnormal time series. This paper introduces Recourse in time series Anomaly Detection (RecAD), a framework for addressing anomalies in multivariate time series using backtracking counterfactual reasoning. By modeling the causes of anomalies as external interventions on exogenous variables, RecAD predicts recourse actions to restore normal status as counterfactual explanations, where the recourse function, responsible for generating actions based on observed data, is trained using an end-to-end approach. Experiments on synthetic and real-world datasets demonstrate its effectiveness.
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