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

• URL: http://arxiv.org/abs/2411.18043v1
• Date: Wed, 27 Nov 2024 04:25:13 GMT
• Title: Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification
• Authors: Mingsen Du, Meng Chen, Yongjian Li, Cun Ji, Shoushui Wei,
• Abstract summary: We propose a heterogeneous relationships of subjects and shapelets method for semi-supervised MTS classification. We first utilize a contrast temporal self-attention module to obtain sparse MTS representations. Secondly, we learn the shapelets for different subject types, incorporating both the subject features and their shapelets as additional information. Finally, we use a dual level graph attention network to get prediction.
• Score: 4.4881185098082
• License:
• 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.

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