Automatic Classification Rules for Anomaly Detection in Time-Series

Anomaly detection in time-series is an important issue in many applications. It is particularly hard to accurately detect multiple anomalies in time-series. Pattern discovery and rule extraction are effective solutions for allowing multiple anomaly detection. In this paper, we define a Composition-based Decision Tree algorithm that automatically discovers and generates human-understandable classification rules for multiple anomaly detection in time-series. To evaluate our solution, our algorithm is compared to other anomaly detection algorithms on real datasets and benchmarks.