STAD: State-Transition-Aware Anomaly Detection Under Concept Drifts

ABSTRACT

The detection of temporal abnormal patterns over streaming data is challenging due to volatile data properties and lacking real-time labels. The abnormal patterns are usually hidden in the temporal context, which can not be detected by evaluating single points. Furthermore, the normal state evolves over time due to concept drift. A single model does not fit all data over time. Autoencoders are recently applied for unsupervised anomaly detection. However, they usually get expired and invalid after distributional drifts in the data stream. In this paper, we propose an autoencoder-based approach (STAD) for anomaly detection under concept drift. In particular, we use a state-transition-based model to map different data distributions in each period of the data stream into states, thereby addressing the model adaptation problem in an interpretable way. We empirically demonstrate the state transition process and evaluate the anomaly detection performance on the Covid-19 dataset of Germany. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)