Regularizing autoencoders with wavelet transform for sequence anomaly detection

Abstract

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 indicate 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 statistical 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.