User-driven Error Detection for Time Series with Events

Anomalies are pervasive in time series data, such as sensor readings. Existing methods for anomaly detection cannot distinguish between anomalies that represent data errors, such as incorrect sensor readings, and notable events, such as the watering action in soil monitoring. In addition, the quality performance of such detection methods highly depends on the configuration parameters, which are dataset specific. In this work, we exploit active learning to detect both errors and events in a single solution that aims at minimizing user interaction. For this joint detection, we introduce an algorithm that accurately detects and labels anomalies with a non-parametric concept of neighborhood and probabilistic classification. Given a desired quality, the confidence of the classification is then used as termination condition for the active learning algorithm. Experiments on real and synthetic datasets demonstrate that our approach achieves F-score above 80% in detecting errors by labeling 2 to 5 points in one data series. We also show the superiority of our solution compared to the state-of-the-art approaches for anomaly detection. Finally, we demonstrate the positive impact of our error detection methods in downstream data repairing algorithms.