Detecting Anomalies in Time Series Using Kernel Density Approaches-Reference-Cited by-同舟云学术

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Detecting Anomalies in Time Series Using Kernel Density Approaches

Published:2024 Issue: Volume:12 Page:33420-33439
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Frehner Robin¹^ORCID,Wu Kesheng²^ORCID,Sim Alexander²^ORCID,Kim Jinoh³^ORCID,Stockinger Kurt¹^ORCID

Affiliation:

1. School of Engineering, Zurich University of Applied Sciences, Winterthur, Switzerland

2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

3. Department of Computer Science and Information Systems, Texas A&M University--Commerce, Commerce, TX, USA

Funder

Office of Advanced Scientific Computing Research, Office of Science, U.S. Department of Energy

National Energy Research Scientific Computing Center

School of Engineering, Zurich University of Applied Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6287639/10380310/10453576-aam.pdf

Reference49 articles.

1. A Review on Outlier/Anomaly Detection in Time Series Data

2. Anomaly Detection for IoT Time-Series Data: A Survey

3. Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction

4. Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series

5. A hybrid approach for alarm verification using stream processing, machine learning and text analytics;Sima

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