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.

翻译：近年来，时间序列分类吸引了大量研究者的关注，已有数百种方法被提出。然而，这些方法往往忽略了维度间的空间相关性以及特征间的局部相关性。为解决这一问题，本研究提出了一种基于因果与局部相关性的网络（CaLoNet）用于多元时间序列分类。首先，利用因果建模对维度间的成对空间相关性进行建模，以获取图结构。随后，采用关系提取网络融合局部相关性，以获取长期依赖特征。最后，将图结构与长期依赖特征整合到图神经网络中。在UEA数据集上的实验表明，与现有先进方法相比，CaLoNet能够获得具有竞争力的性能。