Multivariate Time-Series Representation Learning via Hierarchical Correlation Pooling Boosted Graph Neural Network

Abstract

Representation learning is vital for the performance of Multivariate Time Series (MTS) related tasks. Given high-dimensional MTS data, researchers generally rely on deep learning (DL) models to learn representative features. Among them, the methods that can capture the spatial-temporal dependencies within MTS data generally achieve better performance. However, they ignored hierarchical relations and the dynamic property within MTS data, hindering their performance. To address these problems, we propose a Hierarchical Correlation Pooling boosted graph neural network (HierCorrPool) for MTS data representation learning. First, we propose a novel correlation pooling scheme to learn and capture hierarchical correlations between sensors. Meanwhile, a new assignment matrix is designed to ensure the effective learning of hierarchical correlations by adaptively combining both sensor features and correlations. Second, we learn sequential graphs to represent the dynamic property within MTS data, so that this property can be captured for learning decent representations. We conducted extensive experiments to test our model on various MTS tasks, including remaining useful life prediction, human activity recognition, and sleep stage classification. Experimental results have shown the effectiveness of our proposed model.