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Unsupervised anomaly detection in time-series

Abstract : Anomaly detection in multivariate time series is a major issue in many fields. The increasing complexity of systems and the explosion of the amount of data have made its automation indispensable. This thesis proposes an unsupervised method for anomaly detection in multivariate time series called USAD. However, deep neural network methods suffer from a limitation in their ability to extract features from the data since they only rely on local information. To improve the performance of these methods, this thesis presents a feature engineering strategy that introduces non-local information. Finally, this thesis proposes a comparison of sixteen time series anomaly detection methods to understand whether the explosion in complexity of neural network methods proposed in the current literature is really necessary.
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Submitted on : Monday, May 30, 2022 - 3:44:27 PM
Last modification on : Friday, June 3, 2022 - 6:37:06 PM


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  • HAL Id : tel-03681871, version 1


Julien Audibert. Unsupervised anomaly detection in time-series. Neural and Evolutionary Computing [cs.NE]. Sorbonne Université, 2021. English. ⟨NNT : 2021SORUS358⟩. ⟨tel-03681871⟩



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