EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly
  Detection in Multivariate Time Series
Jie Liu Qilin Li Senjian An Bradley Ezard Ling Li
Transformer-based models for anomaly detection in multivariate time series
can benefit from the self-attention mechanism due to its advantage in modeling
long-term dependencies. However, Transformer-based anomaly detection models
have problems such as a large amount of data being required for training,
standard positional encoding is not suitable for multivariate time series data,
and the interdependence between time series is not considered. To address these
limitations, we propose a novel anomaly detection method, named EdgeConvFormer,
which integrates Time2vec embedding, stacked dynamic graph CNN, and Transformer
to extract global and local spatial-time information. This design of
EdgeConvFormer empowers it with decomposition capacities for complex time
series, progressive spatiotemporal correlation discovery between time series,
and representation aggregation of multi-scale features. Experiments demonstrate
that EdgeConvFormer can learn the spatial-temporal correlations from
multivariate time series data and achieve better anomaly detection performance
than the state-of-the-art approaches on many real-world datasets of different
scales.