通过因果与局部相关性建模，提升多变量时间序列分类效果
Causal and Local Correlations Based Network for Multivariate Time Series Classification
- 用因果建模捕捉维度间空间相关性，构建图结构
- 融合局部相关性提取长期依赖特征，提升表示能力
- 在UEA数据集上性能优于主流方法，适合时序关系建模
近期，时间序列分类吸引了大量研究关注，已有数百种方法被提出。然而，这些方法常忽视维度间的空间相关性以及特征间的局部相关性。为此，本文提出基于因果与局部相关性的网络（CaLoNet）用于多变量时间序列分类。首先，通过因果建模对维度间的成对空间相关性进行建模，获取图结构；其次，利用关系提取网络融合局部相关性，提取长期依赖特征；最后，将图结构与长期依赖特征整合至图神经网络中。在UEA数据集上的实验表明，CaLoNet相较于当前最优方法可获得具有竞争力的性能。
原文摘要 · Abstract (English)
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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