  Anomaly detection of multivariate time series is meaningful for system
behavior monitoring. This paper proposes an anomaly detection method based on
unsupervised Short- and Long-term Mask Representation learning (SLMR). The main
idea is to extract short-term local dependency patterns and long-term global
trend patterns of the multivariate time series by using multi-scale residual
dilated convolution and Gated Recurrent Unit(GRU) respectively. Furthermore,
our approach can comprehend temporal contexts and feature correlations by
combining spatial-temporal masked self-supervised representation learning and
sequence split. It considers the importance of features is different, and we
introduce the attention mechanism to adjust the contribution of each feature.
Finally, a forecasting-based model and a reconstruction-based model are
integrated to focus on single timestamp prediction and latent representation of
time series. Experiments show that the performance of our method outperforms
other state-of-the-art models on three real-world datasets. Further analysis
shows that our method is good at interpretability.
