Causal and Local Correlations Based Network for Multivariate Time Series Classification

• URL: http://arxiv.org/abs/2411.18008v1
• Date: Wed, 27 Nov 2024 02:54:26 GMT
• Title: Causal and Local Correlations Based Network for Multivariate Time Series Classification
• Authors: Mingsen Du, Yanxuan Wei, Xiangwei Zheng, Cun Ji,
• Abstract summary: This study proposes the causal and local correlations based network (CaLoNet) for time series classification. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods.
• Score: 1.0499611180329804
• License:
• Abstract: Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time series classification. First, pairwise spatial correlations between dimensions are modeled using causality modeling to obtain the graph structure. Then, a relationship extraction network is used to fuse local correlations to obtain long-term dependency features. Finally, the graph structure and long-term dependency features are integrated into the graph neural network. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods.

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