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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection

verfasst von : Andreas Theissler, Manuel Wengert, Felix Gerschner

Erschienen in: Advances in Intelligent Data Analysis XXI

Verlag: Springer Nature Switzerland

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Abstract

The analysis of time series data is of high relevance in fields like manufacturing, health, automotive, or science. In this paper, we propose ROCKAD, a kernel-based approach for semi-supervised whole time series anomaly detection, i.e. the assignment of a single anomaly score to an entire time series. Our key idea is to use ROCKET as an unsupervised feature extractor and to train a single as well as an ensemble of k-nearest neighbors anomaly detectors to deduce an anomaly score. To the best of our knowledge, this is the first approach to transfer the ideas of ROCKET to the task of anomaly detection. We systematically evaluate ROCKAD for univariate time series and show it is statistically significantly better compared to baseline methods. Additionally, we show in a case study that ROCKAD is also applicable to multivariate time series.

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Vorheriges Kapitel Should We Consider On-Demand Analysis in Scale-Free Networks?
Nächstes Kapitel Out-of-Distribution Generalisation with Symmetry-Based Disentangled Representations
Fußnoten
1
ROCKAD source code and further information: https://​ml-and-vis.​org/​rockad.
 
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Metadaten
Titel
ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection
verfasst von
Andreas Theissler
Manuel Wengert
Felix Gerschner
Copyright-Jahr
2023
Buch
Advances in Intelligent Data Analysis XXI

Print ISBN: 978-3-031-30046-2

Electronic ISBN: 978-3-031-30047-9

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-30047-9_33

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