  We present PyTorch Geometric Temporal a deep learning framework combining
state-of-the-art machine learning algorithms for neural spatiotemporal signal
processing. The main goal of the library is to make temporal geometric deep
learning available for researchers and machine learning practitioners in a
unified easy-to-use framework. PyTorch Geometric Temporal was created with
foundations on existing libraries in the PyTorch eco-system, streamlined neural
network layer definitions, temporal snapshot generators for batching, and
integrated benchmark datasets. These features are illustrated with a
tutorial-like case study. Experiments demonstrate the predictive performance of
the models implemented in the library on real world problems such as
epidemiological forecasting, ridehail demand prediction and web-traffic
management. Our sensitivity analysis of runtime shows that the framework can
potentially operate on web-scale datasets with rich temporal features and
spatial structure.
