Graph Neural Networks (GNNs) are becoming central in the study of time series, coupled with existing algorithms as Temporal Convolutional Networks and Recurrent Neural Networks. In this paper, we see time series themselves as directed graphs, so that their topology encodes time dependencies and we start to explore the effectiveness of GNNs architectures on them. We develop two distinct Geometric Deep Learning models, a supervised classifier and an autoencoder-like model for signal reconstruction. We apply these models on a quality recognition problem.

翻译：图神经网络（GNNs）正逐渐成为时间序列研究中的核心方法，与现有的时序卷积网络和循环神经网络等算法协同作用。本文将时间序列本身视为有向图，通过其拓扑结构编码时间依赖性，并开始探索GNN架构在此类数据上的有效性。我们开发了两种不同的几何深度学习模型：一种监督分类器，以及一种类似自编码器的信号重建模型。我们将这些模型应用于质量识别问题。