  The demand for high-performance anomaly detection techniques of IoT data
becomes urgent, especially in industry field. The anomaly identification and
explanation in time series data is one essential task in IoT data mining. Since
that the existing anomaly detection techniques focus on the identification of
anomalies, the explanation of anomalies is not well-solved. We address the
anomaly explanation problem for multivariate IoT data and propose a 3-step
self-contained method in this paper. We formalize and utilize the domain
knowledge in our method, and identify the anomalies by the violation of
constraints. We propose set-cover-based anomaly explanation algorithms to
discover the anomaly events reflected by violation features, and further
develop knowledge update algorithms to improve the original knowledge set.
Experimental results on real datasets from large-scale IoT systems verify that
our method computes high-quality explanation solutions of anomalies. Our work
provides a guide to navigate the explicable anomaly detection in both IoT fault
diagnosis and temporal data cleaning.
