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A Hybrid Anomaly Detection Method for Multivariate Time Series — arXiv:2611.01661

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01661
License (as archived): CC0-1.0
Archived: 2026-11-03T10:47:50.402Z
SHA-256 of the archived text: ac091c714316fe7891635341494cc19f74d699b0496fd1dd63e562d720698bf4
Stable URL of this page: https://metraipolis.com/documento/dc_331b45f9728d

How to cite

A Hybrid Anomaly Detection Method for Multivariate Time Series — arXiv:2611.01661 — arXiv (en), https://arxiv.org/abs/2611.01661 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_331b45f9728d (Metraipolis archive dc_331b45f9728d, sha256 ac091c714316fe7891635341494cc19f74d699b0496fd1dd63e562d720698bf4)

BibTeX

@misc{dc331b45f9728d,
  title = {A Hybrid Anomaly Detection Method for Multivariate Time Series — arXiv:2611.01661},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01661},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_331b45f9728d; sha256 ac091c714316fe7891635341494cc19f74d699b0496fd1dd63e562d720698bf4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01661. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multivariate time series are ubiquitous in monitoring smart systems such as power plants, smart houses, and servers. These systems use multiple sensors to collect the system's status over time. Those sensors represent the status of the whole system as one entity. Anomaly detection in such smart systems is of critical importance to prevent system failure and maintain functionality. But anomaly detection in such multivariate time series data is very challenging since this type of data includes both temporal and spatial information. Capturing both of these pieces of information is important to detect anomalies. In this paper, we propose a hybrid deep learning-based anomaly detection method, which captures both temporal and spatial information. We propose to take advantage of the feature extraction capabilities of 2d convolutional network in a variational autoencoder, alongside a prediction-based deep neural network of 2d convolutional networks and a gated recurrent unit network to capture both spatial and temporal information. Experiments on public data against state-of-the-art show the potential of our method.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01661 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:39:50.503Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).