Feature Bagging with Nested Rotations (FBNR) for anomaly detection in multivariate time series

Anastasios Iliopoulos, John Violos, Christos Diou, Iraklis Varlamis

Abstract: Highlights•Focuses on anomaly detection in multivariate time series.•Introduces an ensemble machine learning method for anomaly detection.•Combines feature bagging and nested PCA rotations to enhance detectors variety.•Demonstrates effectiveness through experiments on two datasets.