Anomaly Detection Method for Time Series Data Based on Transformer Reconstruction

Abstract

Multiple temporal anomaly detection algorithms have important research significance in many application fields, such as system state estimation, fault prediction and diagnosis, network behavior anomaly detection and so on. Aiming at the problems of abnormal noise, high dimensionality, lack of labeling, and difficulty in learning abnormal features of various temporal data, an anomaly detection model TRAD based on Transformer reconstruction was proposed, which used self-conditioning to extract robust multi-modal features to obtain the stability of training. At the same time, the adversarial training process is used to amplify the reconstruction error. Experiments on three public datasets show that the proposed model not only has excellent detection performance, but also has strong applicability and generalization ability for unknown heterogeneous time series data.