Causal and Local Correlations Based Network for Multivariate Time Series Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913587445891072 |
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| author | Du, Mingsen Wei, Yanxuan Zheng, Xiangwei Ji, Cun |
| author_facet | Du, Mingsen Wei, Yanxuan Zheng, Xiangwei Ji, Cun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal and Local Correlations Based Network for Multivariate Time Series Classification Du, Mingsen Wei, Yanxuan Zheng, Xiangwei Ji, Cun Machine Learning Artificial Intelligence Methodology 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. |
| title | Causal and Local Correlations Based Network for Multivariate Time Series Classification |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2411.18008 |