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Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series

Rewicki, Ferdinand and Denzler, Joachim and Niebling, Julia (2023) Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series. Applied Sciences. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/app13031778. ISSN 2076-3417.

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Official URL: https://www.mdpi.com/2076-3417/13/3/1778


Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly-type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types.

Item URL in elib:https://elib.dlr.de/193733/
Document Type:Article
Title:Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Rewicki, FerdinandUNSPECIFIEDhttps://orcid.org/0000-0003-2264-9495UNSPECIFIED
Denzler, JoachimUNSPECIFIEDhttps://orcid.org/0000-0002-3193-3300UNSPECIFIED
Niebling, JuliaUNSPECIFIEDhttps://orcid.org/0000-0001-5413-2234UNSPECIFIED
Date:30 January 2023
Journal or Publication Title:Applied Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Special Issue "Unsupervised Anomaly Detection"
Keywords:anomaly detection; time series; machine learning; deep learning; benchmark
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - EDEN ISS Follow-on
Location: Jena
Institutes and Institutions:Institute of Data Science > Data Analysis and Intelligence
Deposited By: Rewicki, Ferdinand
Deposited On:14 Feb 2023 14:00
Last Modified:14 Feb 2023 14:00

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