Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series (Prof. Dr. Altan Çakır)
This paper presents an empirical study on integrating dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on MUTANT and Anomaly-Transformer. Evaluated across MSL, SMAP, and SWaT datasets, the study examines PCA, UMAP, Random Projection, and t-SNE techniques. Findings show dimensionality reduction enhances anomaly detection performance and significantly reduces training times, particularly with UMAP for the MUTANT model. The study highlights the importance of selecting appropriate dimensionality reduction strategies for improved efficiency and accuracy in anomaly detection.