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The demand for high-performance anomaly detection techniques of IoT databecomes urgent, especially in industry field. The anomaly identification andexplanation in time series data is one essential task in IoT data mining. Sincethat the existing anomaly detection techniques focus on the identification ofanomalies, the explanation of anomalies is not well-solved. We address theanomaly explanation problem for multivariate IoT data and propose a 3-stepself-contained method in this paper. We formalize and utilize the domainknowledge in our method, and identify the anomalies by the violation ofconstraints. We propose set-cover-based anomaly explanation algorithms todiscover the anomaly events reflected by violation features, and furtherdevelop knowledge update algorithms to improve the original knowledge set.Experimental results on real datasets from large-scale IoT systems verify thatour method computes high-quality explanation solutions of anomalies. Our workprovides a guide to navigate the explicable anomaly detection in both IoT faultdiagnosis and temporal data cleaning.

Exploring Data and Knowledge combined Anomaly Explanation of Multivariate Industrial Data