Multivariate time series anomaly detection based on sparse autoencoder and dynamic graph attention network

With the further advancement of industrial technology, the data generated by sensors is gradually becoming more complex. Deep learning approaches have made notable strides in the domain of anomaly detection, especially when it comes to high-dimensional datasets. However, most existing methods neglect the complex relationships between feature dimensions and the dependencies between multiple time steps, resulting in insufficient extraction of key feature information. In response to this problem, our paper introduces a model for detecting anomalies in multivariate time series, utilizing Sparse Autoencoder and Dynamic Graph Attention Network (SAE-GATv2). The outcomes of experiments conducted on two actual sensor datasets reveal that our suggested approach is capable of identifying anomalies with greater precision and showcases superior overall performance in comparison to the baseline methods.