KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent Units Article Swipe
Issam Ait Yahia
,
Ismaïl Berrada
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.17976
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.17976
Anomaly detection in real-world time-series data is a challenging task due to the complex and nonlinear temporal dynamics involved. This paper introduces KoopAGRU, a new deep learning model designed to tackle this problem by combining Fast Fourier Transform (FFT), Deep Dynamic Mode Decomposition (DeepDMD), and Koopman theory. FFT allows KoopAGRU to decompose temporal data into time-variant and time-invariant components providing precise modeling of complex patterns. To better control these two components, KoopAGRU utilizes Gate Recurrent Unit (GRU) encoders to learn Koopman observables, enhancing the detection capability across multiple temporal scales. KoopAGRU is trained in a single process and offers fast inference times. Extensive tests on various benchmark datasets show that KoopAGRU outperforms other leading methods, achieving a new average F1-score of 90.88\% on the well-known anomalies detection task of times series datasets, and proves to be efficient and reliable in detecting anomalies in real-world scenarios.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.17976
- https://arxiv.org/pdf/2501.17976
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407012832
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407012832Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.17976Digital Object Identifier
- Title
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KoopAGRU: A Koopman-based Anomaly Detection in Time-Series using Gated Recurrent UnitsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-29Full publication date if available
- Authors
-
Issam Ait Yahia, Ismaïl BerradaList of authors in order
- Landing page
-
https://arxiv.org/abs/2501.17976Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2501.17976Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2501.17976Direct OA link when available
- Concepts
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Series (stratigraphy), Anomaly detection, Anomaly (physics), Computer science, Time series, Artificial intelligence, Geology, Physics, Machine learning, Condensed matter physics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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