Regularizing autoencoders with wavelet transform for sequence anomaly detection

Nowadays, systems or entities are usually monitored by devices, generating large amounts of time series. Detecting anomalies in them help prevent potential losses, thus arousing much research interest. Existing studies have adopted autoencoders to detect anomalies, where reconstruction errors are used to indi-cate outliers. However, sometimes autoencoders may also reconstruct anomalies well due to the learned general features in latent spaces. To solve the above problem, we propose to regularize autoencoders to grasp specific features of normal sequences. Specifically, spectral unique patterns are captured by sta-tistical analysis on discrete wavelet transform (DWT) coefficients of input sequences, restricting latent spaces to reflect unique patterns of normal sequences in both time and frequency domains. Furthermore, a Weight Controller calculating sample-adaptive regularization weights is designed to fully utilize the regularization effect. Extensive experiments on three public benchmarks demonstrate the effectiveness and superiority of the proposed model compared with state-of-the-art algorithms. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a regularized autoencoder model is proposed to capture specific features of normal sequences for anomaly detection. By performing statistical analysis on input sequences, spectral unique patterns are captured and a Weight Controller is designed to calculate sample-adaptive regularization weights, leading to improved effectiveness and superiority compared with state-of-the-art algorithms.