Anomaly detection algorithm based on graph neural network for missing multivariate time series

Addressing the issue of anomaly detection on missing multivariate time series data in real IoT (Internet of things) environments, a novel method on multivariate time series anomaly detection algorithm intergrated with graph embedding of missing information was proposed. Using a joint learning framework of pre-interpolation and anomaly detection task fusion, a GNN (graph neural network) pre-interpolation module based on time series Gaussian kernel function was designed to realize the joint optimization of pre-interpolation and anomaly detection task. A graph structure learning method for embedding missing information in time series data was proposed, using graph attention mechanism to fuse missing information masking matrix and spatiotemporal feature vectors, effectively modeling the potential connections of missing data distribution in multivariate time series. The performance of the algorithm was verified on real IoT sensor datasets. Experimental results prove that the proposed method significantly outperform the mainstream two-stage methods on the task of missing multivariate time series anomaly detection. The comparative experiment of the pre-interpolation module fully prove the effectiveness of the GNN pre-interpolation layer based on the Gaussian kernel function.