by
Zeynep Kalaycıoğlu | Tem 03, 2024
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.
The paper explores integrating dimensionality reduction techniques with advanced unsupervised time series anomaly detection models, focusing on the MUTANT and Anomaly-Transformer models. It emphasizes the significance of anomaly detection in multivariate time series data, which is crucial for identifying critical events or system malfunctions. The study evaluates the performance of these models across three challenging datasets: MSL, SMAP, and SWaT, each presenting unique complexities for anomaly detection assessment. Four key dimensionality reduction techniques are examined: PCA, UMAP, t-SNE, and Random Projection. These methods simplify high-dimensional datasets while maintaining essential information, enhancing anomaly detection performance. Results indicate that dimensionality reduction not only aids in reducing computational complexity but also significantly improves anomaly detection capabilities in certain scenarios. The study observes notable reductions in training times, highlighting the efficiency gains achieved through dimensionality reduction. The MUTANT model demonstrates adaptability, particularly with UMAP reduction, while the Anomaly Transformer model showcases versatility across various reduction techniques. The research underscores the importance of selecting appropriate dimensionality reduction strategies based on specific model requirements and dataset characteristics. By combining dimensionality reduction with advanced anomaly detection models, the study provides valuable insights into the synergistic effects of these techniques, contributing to the advancement of anomaly detection in time series data analysis. The findings emphasize the need for efficient, accurate, and scalable solutions in anomaly detection, paving the way for future research directions in this field.
