TCAD: Unsupervised Anomaly Detection Based on Global Local Representation Differences

Abstract

Multivariate time series anomaly detection is of great interest because of its wide range of applications. Since it is difficult to obtain accurate anomaly labels, many unsupervised anomaly detection algorithms have been developed. However, it is challenging to build an unsupervised multivariate anomaly detection model because we need to find a criterion with anomaly discriminative power. Previously, researchers have focused on extracting the association of time points with global sequences, while ignoring the association of time points with local sequences. In this paper, we propose a combined model TCAD based on Transformer and Resnet, which learns global and local features of sequences using Transformer and Resnet, and constrains the learning of rich global local representations using reconstructed differences and global local representation differences. In addition, this paper proposes an anomaly score based on global and local feature discrepancies. TCAD is extensively tested on four public datasets and two private datasets.

Keywords

• Anomaly detection
• unsupervised learning
• transformer
• Resnet
• multivariate time series