Algorithmic Recourse in Abnormal Multivariate Time Series
It addresses algorithmic recourse for multivariate time series, a domain with limited prior research, offering a solution for enhancing transparency in anomaly detection systems.
The paper tackles the problem of providing actionable recommendations to reverse abnormal predictions in multivariate time series, introducing the RecAD framework which uses backtracking counterfactual reasoning to predict recourse actions, with experiments showing its effectiveness on synthetic and real-world datasets.
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.