Recent studies have shown that autoencoder-based models can achieve superior
performance on anomaly detection tasks due to their excellent ability to fit
complex data in an unsupervised manner. In this work, we propose a novel
autoencoder-based model, named StackVAE-G that can significantly bring the
efficiency and interpretability to multivariate time series anomaly detection.
Specifically, we utilize the similarities across the time series channels by
the stacking block-wise reconstruction with a weight-sharing scheme to reduce
the size of learned models and also relieve the overfitting to unknown noises
in the training data. We also leverage a graph learning module to learn a
sparse adjacency matrix to explicitly capture the stable interrelation
structure among multiple time series channels for the interpretable pattern
reconstruction of interrelated channels. Combining these two modules, we
introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph
neural network) model for multivariate time series anomaly detection. We
conduct extensive experiments on three commonly used public datasets, showing
that our model achieves comparable (even better) performance with the
state-of-the-art modelsand meanwhile requires much less computation and memory
cost. Furthermore, we demonstrate that the adjacency matrix learned by our
model accurately captures the interrelation among multiple channels, and can
provide valuable information for failure diagnosis applications.Comment: Accepted to AI Ope