DragStream: An Anomaly And Concept Drift Detector In Univariate Data Streams

Abstract

Anomaly detection in data streams comes with different technical challenges due to the data nature. The main challenges include storage limitations, the speed of data arrival, and concept drifts. In the literature, methods for mining data streams in order to detect anomalies have been proposed. While some methods focus on tackling a specific issue, other methods handle diverse problems but may have high complexity (time and memory). In the present work, we propose DragStream, a novel subsequence anomaly and concept drift detection algorithm for univariate data streams. DragStream extends the subsequence anomaly detection method for time series data Drag to streaming data. Furthermore, the new method is inspired by the well-known Matrix Profile, Drag, and MILOF which are respectively point and subsequence anomaly detection methods for time series and data streams. We conducted intensive experiments and statistical analysis to evaluate the performance of the proposed approach against existing methods. The results show that our method is competitive in performance while being linear in time and memory complexity. Finally, we provide an open-source implementation of the new method.