RESTAD: REconstruction and Similarity based Transformer for time series Anomaly Detection

arXiv:2405.07509v1 Announce Type: new
Abstract: Anomaly detection in time series data is crucial across various domains. The scarcity of labeled data for such tasks has increased the attention towards unsupervised learning methods. These approaches, often relying solely on reconstruction error, typically fail to detect subtle anomalies in complex datasets. To address this, we introduce RESTAD, an adaptation of the Transformer model by incorporating a layer of Radial Basis Function (RBF) neurons within its architecture. This layer fits a non-parametric density …

abstract anomaly anomaly detection arxiv attention cs.ai cs.lg data datasets detection domains error series tasks time series transformer type unsupervised unsupervised learning