Preserving Performance in Anomaly Detection Models for Real-Time Univariate Streams

Abstract

Anomaly detection has gathered plenty of attention in the previous years. However, there is little evidence of the fact that existing anomaly detection models could show similar performance on different streaming datasets.
Within this study, we research the applicability of existing anomaly detectors to a wide range of univariate streams. We identify main dependent factors with time series that might influence the difference in performances of popular anomaly detection models across different streams, namely, time series features, data drifts, and disorder. We explore the effects that each of the dependent factors has on the performance of selected anomaly detectors. Based on our findings, we propose an adaptive threshold technique that monitors time series disorder. This technique can be integrated into built-in threshold of various anomaly detectors. We show the usability of the proposed method in improving the performance of the models on selected datasets.