Year: 2025
Type: article
Abstract: Time series anomaly detection in streaming environments faces persistent challenges due to concept drift, which gradually degrades model reliability. In this paper, we propose Anomaly Detection with Drift-Aware Ensemble-based Incremental Learning (ADDAEIL), an unsupervised anomaly detection framework that incrementally adapts to concept drift in non-stationary streaming time series data. ADDAEIL integrates a hybrid drift detection mechanism that combines statistical distribution tests with structural-based performance evaluation of base detectors in Isolation Forest. This design enables unsupervised detection and continuous adaptation to evolving data patterns. Based on the estimated drift intensity, an adaptive update strategy selectively replaces degraded base detectors. This allows the anomaly detection model to incorporate new information while preserving useful historical behavior. Experiments on both real-world and synthetic datasets show that ADDAEIL consistently outperforms existing state-of-the-art methods and maintains robust long-term performance in non-stationary data streams.
Source: Algorithms
DOI: 10.3390/a18060359
Institution: University of Auckland
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
Funders: Tertiary Education Commission, QuakeCoRE
OpenAlex ID: W4411211396