Related papers: Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

URL: http://arxiv.org/abs/2402.02820v1
Date: Mon, 5 Feb 2024 09:06:57 GMT
Title: Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Authors: Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, Qingwei Lin, Haiming Zhang, Jianhui Li, Gaogang Xie
Abstract summary: Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities. FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) Our approach has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods.
Score: 40.21603048003118
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed "target attention" mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.