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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30276
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dc.contributor.authorFrehner, Robin-
dc.contributor.authorWu, Kesheng-
dc.contributor.authorSim, Alexander-
dc.contributor.authorKim, Jinoh-
dc.contributor.authorStockinger, Kurt-
dc.date.accessioned2024-03-16T09:48:49Z-
dc.date.available2024-03-16T09:48:49Z-
dc.date.issued2024-03-
dc.identifier.issn2169-3536de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30276-
dc.description.abstractThis 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.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Accessde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectTime series anomaly detectionde_CH
dc.subjectMachine learningde_CH
dc.subjectNeural networkde_CH
dc.subjectAutoencoderde_CH
dc.subjectKernel density estimationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDetecting anomalies in time series using kernel density approachesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/ACCESS.2024.3371891de_CH
dc.identifier.doi10.21256/zhaw-30276-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end33439de_CH
zhaw.pages.start33420de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume12de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedIntelligent Information Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Frehner, R., Wu, K., Sim, A., Kim, J., & Stockinger, K. (2024). Detecting anomalies in time series using kernel density approaches. IEEE Access, 12, 33420–33439. https://doi.org/10.1109/ACCESS.2024.3371891
Frehner, R. et al. (2024) ‘Detecting anomalies in time series using kernel density approaches’, IEEE Access, 12, pp. 33420–33439. Available at: https://doi.org/10.1109/ACCESS.2024.3371891.
R. Frehner, K. Wu, A. Sim, J. Kim, and K. Stockinger, “Detecting anomalies in time series using kernel density approaches,” IEEE Access, vol. 12, pp. 33420–33439, Mar. 2024, doi: 10.1109/ACCESS.2024.3371891.
FREHNER, Robin, Kesheng WU, Alexander SIM, Jinoh KIM und Kurt STOCKINGER, 2024. Detecting anomalies in time series using kernel density approaches. IEEE Access. März 2024. Bd. 12, S. 33420–33439. DOI 10.1109/ACCESS.2024.3371891
Frehner, Robin, Kesheng Wu, Alexander Sim, Jinoh Kim, and Kurt Stockinger. 2024. “Detecting Anomalies in Time Series Using Kernel Density Approaches.” IEEE Access 12 (March): 33420–39. https://doi.org/10.1109/ACCESS.2024.3371891.
Frehner, Robin, et al. “Detecting Anomalies in Time Series Using Kernel Density Approaches.” IEEE Access, vol. 12, Mar. 2024, pp. 33420–39, https://doi.org/10.1109/ACCESS.2024.3371891.


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