Publication Title
IEEE Access
Document Type
Article
Abstract/Description
This paper introduces a novel anomaly detection approach tailored for time series data with exclusive reliance on normal events during training. Our key innovation lies in the application of kernel-density estimation (KDE) to scrutinize reconstruction errors, providing an empirically derived probability distribution for normal events post-reconstruction. This non-parametric density estimation technique offers a nuanced understanding of anomaly detection, differentiating it from prevalent threshold-based mechanisms in existing methodologies. In post-training, events are encoded, decoded, and evaluated against the estimated density, providing a comprehensive notion of normality. In addition, we propose a data augmentation strategy involving variational autoencoder-generated events and a smoothing step for enhanced model robustness. The significance of our autoencoder-based approach is evident in its capacity to learn normal representation without prior anomaly knowledge. Through the KDE step on reconstruction errors, our method addresses the versatility of anomalies, departing from assumptions tied to larger reconstruction errors for anomalous events. Our proposed likelihood measure then distinguishes normal from anomalous events, providing a concise yet comprehensive anomaly detection solution. The extensive experimental results support the feasibility of our proposed method, yielding significantly improved classification performance by nearly 10% on the UCR benchmark data.
Department
Computer Science and Information Systems
First Page
33420
Last Page
33439
DOI
10.1109/ACCESS.2024.3371891
Volume
12
ISSN
2169-3536
Date
3-7-2024
Citation Information
Frehner, Robin; Wu, Kesheng; Sim, Alexander; and Kim, Jinoh, "Detecting Anomalies in Time Series Using Kernel Density Approaches" (2024). Faculty Publications. 196.
https://digitalcommons.tamuc.edu/cose-faculty-publications/196
