SAGoG: Similarity-Aware Graph of Graphs Neural Networks for Multivariate Time Series Classification

Shun Wang, Yong Zhang, Xuanqi Lin, Yongli Hu, Qingming Huang, Baocai Yin

Abstract: Multivariate Time Series Classification (MTSC) has important research significance and practical value. Deep learning models have achieved considerable success in addressing MTSC problems. However, a key challenge faced by existing classification models is how to effectively consider the correlations between time series instances and across channels simultaneously, as well as how to capture the dynamic of these inter-channel correlations over time. Current methods often fall short in these aspects: on one hand, they fail to fully account for the combined effects of inter-instance and inter-channel correlations; on the other hand, they largely overlook the dynamic nature of how inter-channel correlations change over time. To address these issues, we propose a novel graph neural network model, called Similarity-Aware Graph of Graphs neural networks (SAGoG), for multivariate time series classification. This model can comprehensively consider the dependencies between channel-level and instance-level time series, it dynamically learns dependency features through graph structure evolution and graph pooling layers. We conduct experiments on the UEA dataset to validate the SAGoG model, and the results demonstrate its outstanding performance in multivariate time series classification tasks.

External IDs:dblp:journals/tkde/WangZLHHY25