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

• URL: http://arxiv.org/abs/2104.07788v1
• Date: Thu, 15 Apr 2021 21:45:57 GMT
• Title: PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
• Authors: Benedek Rozemberczki and Paul Scherer and Yixuan He and George Panagopoulos and Maria Astefanoaei and Oliver Kiss and Ferenc Beres and Nicolas Collignon and Rik Sarkar
• Abstract summary: PyTorch Temporal is a temporal deep learning framework for neural signal processing. It was created with foundations on existing libraries in the PyTorch eco-system. Experiments show that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.
• Score: 8.572409162523735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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