Algorithmic Recourse for Anomaly Detection in Multivariate Time Series
Xiao Han Lu Zhang Yongkai Wu Shuhan Yuan
Anomaly detection in multivariate time series has received extensive study
due to the wide spectrum of applications. An anomaly in multivariate time
series usually indicates a critical event, such as a system fault or an
external attack. Therefore, besides being effective in anomaly detection,
recommending anomaly mitigation actions is also important in practice yet
under-investigated. In this work, we focus on algorithmic recourse in time
series anomaly detection, which is to recommend fixing actions on abnormal time
series with a minimum cost so that domain experts can understand how to fix the
abnormal behavior. To this end, we propose an algorithmic recourse framework,
called RecAD, which can recommend recourse actions to flip the abnormal time
steps. Experiments on two synthetic and one real-world datasets show the
effectiveness of our framework.