Meta-TadGAN: Time Series Anomaly Detection Using TadGAN with Meta-features

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Silva, IOe (Autor) Outra; Carlos Soares (Autor) FEUP; Cerqueira, V (Autor) FEUP; Rodrigues, A (Autor) Outra; Bastardo, P (Autor) Outra

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence - 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3-6, 2024, Proceedings, Part III

Páginas: 347-358

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

Viana do Castelo, 3 September 2024 through 6 September 2024

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-GVT

DOI: 10.1007/978-3-031-73503-5_28

Abstract (EN): TadGAN is a recent algorithm with competitive performance on time series anomaly detection. The detection process of TadGAN works by comparing observed data with generated data. A challenge in anomaly detection is that there are anomalies which are not easy to detect by analyzing the original time series but have a clear effect on its higher-order characteristics. We propose Meta-TadGAN, an adaptation of TadGAN that analyzes meta-level representations of time series. That is, it analyzes a time series that represents the characteristics of the time series, rather than the original time series itself. Results on benchmark datasets as well as real-world data from fire detectors shows that the new method is competitive with TadGAN. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11