Multivariate time series classification (MTSC) is an important data mining task, which can be effectively solved by popular deep learning technology. Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy. To address this problem, we propose a novel temporal dynamic graph neural network (TodyNet) that can extract hidden spatio-temporal dependencies without undefined graph structure. It enables information flow among isolated but implicit interdependent variables and captures the associations between different time slots by dynamic graph mechanism, which further improves the classification performance of the model. Meanwhile, the hierarchical representations of graphs cannot be learned due to the limitation of GNNs. Thus, we also design a temporal graph pooling layer to obtain a global graph-level representation for graph learning with learnable temporal parameters. The dynamic graph, graph information propagation, and temporal convolution are jointly learned in an end-to-end framework. The experiments on 26 UEA benchmark datasets illustrate that the proposed TodyNet outperforms existing deep learning-based methods in the MTSC tasks.

翻译：多变量时间序列分类（MTSC）是一项重要的数据挖掘任务，可通过流行的深度学习技术有效解决。然而，现有基于深度学习的方法忽视了不同维度间的隐藏依赖关系，且很少考虑时间序列独特的动态特征，导致其缺乏足够的特征提取能力，难以获得满意的分类精度。针对这一问题，我们提出了一种新颖的时序动态图神经网络（TodyNet），该网络能在无明确定义图结构的情况下提取隐藏的时空依赖关系。它通过促进孤立但隐式相互依赖变量间的信息流动，并借助动态图机制捕捉不同时间槽之间的关联，进一步提升了模型的分类性能。同时，由于图神经网络的局限性，图的分层表示难以被学习。因此，我们还设计了一个时序图池化层，通过可学习的时序参数为图学习获取全局图级表示。动态图构建、图信息传播与时序卷积在端到端框架中联合学习。在26个UEA基准数据集上的实验表明，所提出的TodyNet在MTSC任务上优于现有基于深度学习的方法。