Robust Online Detection of Anomalies in Evolving Data Streams with GAN Imputation
Xinyao Xu, Mengna Liu, Xu Cheng, Jianhua Zhang, Feng Xiao, Guangya Yang · 2025
Online anomaly detection is a critical technique in intelligent industrial systems. Concept drift and missing data that arise during the transmission of real-time data streams can severely impact the performance of anomaly detection. Therefore, ensuring the accuracy of online anomaly detection and adaptability to concept drift in the presence of missing values is a significant challenge. In this paper, we propose an online anomaly detection model that integrates an online imputation module based on Generative Adversarial Networks (GANs) to efficiently impute missing data. Additionally, following the dynamic model pool strategy, the model dynamically selects and adjusts the optimal models in the pool to effectively respond to changes in data distribution, ensuring detection performance under complex conditions. We evaluated the model's performance across multiple datasets with concept drift and under varying missing data rates. The results demonstrate that the proposed model not only adapts flexibly to rapidly changing data streams but also exhibits enhanced robustness in the presence of missing data.