DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data

arXiv:2302.10753v3 Announce Type: replace
Abstract: Anomaly detection techniques enable effective anomaly detection and diagnosis in multi-variate time series data, which are of major significance for today's industrial applications. However, establishing an anomaly detection system that can be rapidly and accurately located is a challenging problem due to the lack of anomaly labels, the high dimensional complexity of the data, memory bottlenecks in actual hardware, and the need for fast reasoning. In this paper, we propose an anomaly detection and diagnosis …

anomaly anomaly detection arxiv attention cs.lg data detection multivariate networks series time series type