Towards efficient deep autoencoders for multivariate time series anomaly detection

arXiv:2403.02429v1 Announce Type: new
Abstract: Multivariate time series anomaly detection is a crucial problem in many industrial and research applications. Timely detection of anomalies allows, for instance, to prevent defects in manufacturing processes and failures in cyberphysical systems. Deep learning methods are preferred among others for their accuracy and robustness for the analysis of complex multivariate data. However, a key aspect is being able to extract predictions in a timely manner, to accommodate real-time requirements in different applications. In the …

abstract accuracy anomaly anomaly detection applications arxiv autoencoders cs.ai cs.lg deep learning defects detection industrial instance manufacturing multivariate processes research robustness series systems time series type