Graph Neural Networks and Time Series as Directed Graphs for Quality
  Recognition
Angelica Simonetti Ferdinando Zanchetta
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.