PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

Rozemberczki, Benedek; Scherer, Paul; He, Yixuan; Panagopoulos, George; Riedel, Alexander; Astefanoaei, Maria; Kiss, Oliver; Beres, Ferenc; L'opez, Guzm'an; Collignon, Nicolas; Sarkar, Rik

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.