Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification

TL;DR

This paper introduces a semi-supervised multivariate time series classification method that leverages heterogeneous relationships between subjects and shapelets, integrating multiple information sources for improved accuracy.

Contribution

It proposes a novel approach combining contrast temporal self-attention, soft dynamic time warping, and a dual-level graph attention network to model complex relationships in MTS data.

Findings

Outperforms state-of-the-art methods on multiple datasets.

Effectively integrates diverse information sources.

Achieves higher classification accuracy in semi-supervised settings.

Abstract

Multivariate time series (MTS) classification is widely applied in fields such as industry, healthcare, and finance, aiming to extract key features from complex time series data for accurate decision-making and prediction. However, existing methods for MTS often struggle due to the challenges of effectively modeling high-dimensional data and the lack of labeled data, resulting in poor classification performance. To address this issue, we propose a heterogeneous relationships of subjects and shapelets method for semi-supervised MTS classification. This method offers a novel perspective by integrating various types of additional information while capturing the relationships between them. Specifically, we first utilize a contrast temporal self-attention module to obtain sparse MTS representations, and then model the similarities between these representations using soft dynamic time warping to construct a similarity graph. Secondly, we learn the shapelets for different subject types, incorporating both the subject features and their shapelets as additional information to further refine the similarity graph, ultimately generating a heterogeneous graph. Finally, we use a dual level graph attention network to get prediction. Through this method, we successfully transform dataset into a heterogeneous graph, integrating multiple additional information and achieving precise semi-supervised node classification. Experiments on the Human Activity Recognition, sleep stage classification and University of East Anglia datasets demonstrate that our method outperforms current state-of-the-art methods in MTS classification tasks, validating its superiority.