AEVAE: Adaptive Evolutionary Autoencoder for Anomaly Detection in Time Series

Anomaly detection (AD) has witnessed substantial advancements in recent years due to the increasing need for identifying outliers in various engineering applications that undergo environmental adaptations. Consequently, researchers have focused on developing robust AD methods to enhance system performance. The primary challenge faced by AD algorithms lies in effectively detecting unlabeled abnormalities. This study introduces an adaptive evolutionary autoencoder (AEVAE) approach for AD in time-series data. The proposed methodology leverages the integration of unsupervised machine learning techniques with evolutionary intelligence to classify unlabeled data. The unsupervised learning model employed in this approach is the AE network. A systematic programming framework has been devised to transform AEVAE into a practical and applicable model. The primary objective of AEVAE is to detect and predict outliers in time-series data from unlabeled data sources. The effectiveness, speed, and functionality enhancements of the proposed method are demonstrated through its implementation. Furthermore, a comprehensive statistical analysis based on performance metrics is conducted to validate the advantages of AEVAE in terms of unsupervised AD.