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

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

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 data...

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Bibliographic Details
Main Authors: Mahsun Altin, Altan Cakir
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Time series anomaly detection
dimensionality reduction
unsupervised learning
Online Access:https://ieeexplore.ieee.org/document/10559232/

Internet

https://ieeexplore.ieee.org/document/10559232/

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