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

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Silva, IOe (Author) Other; Carlos Soares (Author) FEUP; Cerqueira, V (Author) FEUP; Rodrigues, A (Author) Other; Bastardo, P (Author) Other

Conference proceedings International

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

Pages: 347-358

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

Viana do Castelo, 3 September 2024 through 6 September 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11