Multivariate Time Series Classification via Heterogeneous Graph Representation

Multivariate time series (MTS) classification is essential in industries, such as healthcare and manufacturing, where it helps extract key features from complex data for decision-making and predictions. However, current MTS classification methods often struggle with high-dimensional data and limited labeled samples, leading to poor performance. To address this, we propose a semisupervised MTS classification method via heterogeneous graph representation. This method provides a new perspective on modeling MTS, which integrates various additional information and captures their relationships. First, we employ a contrast temporal self-attention module to obtain sparse MTS representations. Then, we use soft dynamic time warping to model similarities and construct a similarity graph. Next, we learn shapelets and incorporate both subject features and shapelets into the graph, refining it into a heterogeneous graph. Finally, we apply a dual-level graph attention network for node classification task. Experiments on 19 MTS datasets show that our method outperforms existing state-of-the-art methods, demonstrating its effectiveness for MTS classification task.