Szczegóły publikacji
Opis bibliograficzny
VGG based unsupervised anomaly detection in multivariate time series / Grzegorz JABŁOŃSKI // W: Advanced, contemporary control : proceedings of KKA 2020 – the 20th Polish control conference : [14-16 October, 2020], Łódź, Poland / eds. Andrzej Bartoszewicz, Jacek Kabziński, Janusz Kacprzyk. — Cham : Springer Nature Switzerland AG, cop. 2020. — (Advances in Intelligent Systems and Computing ; ISSN 2194-5357 ; vol. 1196). — ISBN: 978-3-030-50935-4; e-ISBN: 978-3-030-50936-1. — S. 1287–1296. — Bibliogr. s. 1295-1296, Abstr. — Publikacja dostępna online od: 2020-06-24. — Dod. afiliacja: Aptiv, Kraków, Poland
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129306 |
|---|---|
| Data dodania do BaDAP | 2020-07-16 |
| DOI | 10.1007/978-3-030-50936-1_107 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Advances in Intelligent Systems and Computing |
Abstract
Anomaly detection in time series data is a known problem, but recent growth in the number of units that can produce data require models that work on unlabelled and diverse types of data. We propose to adapt the neural network introduced by Simonyan and Zisserman in 2015 called VGG16 and used to detect and classify objects in images. We show that the VGG16 architecture with 2-dimensional convolutions replaced with 1-dimensional version could be a building block of an autoencoder approach to detect anomalies. Additionally we show that the proposed model achieves results that are similar or better than of classical anomaly detection methods.
