ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning

ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning

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 framewor...

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Bibliographic Details
Main Authors: Danlei Li, Nirmal-Kumar C. Nair, Kevin I-Kai Wang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Algorithms
Subjects:
time series anomaly detection
concept drift adaptation
isolation forest
incremental learning
unsupervised method
edge computing
Online Access:https://www.mdpi.com/1999-4893/18/6/359
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