  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.
