Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time Series

arXiv:2403.04429v1 Announce Type: new
Abstract: This paper presents an extensive empirical study on the integration of dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. The study involves a comprehensive evaluation across three different datasets: MSL, SMAP, and SWaT. Each dataset poses unique challenges, allowing for a robust assessment of the models' capabilities in varied contexts. The dimensionality reduction techniques examined include PCA, UMAP, Random Projection, and t-SNE, each offering distinct …

abstract advanced anomaly anomaly detection arxiv cs.lg datasets detection dimensionality evaluation influence integration multivariate paper performance series study time series transformer transformer models type unsupervised