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.
翻译:我们向PyTorch地球物理时空学提供了一个深层次学习框架,其中结合了神经空间信号处理方面的最先进的机器学习算法,图书馆的主要目标是在统一易使用的框架中为研究人员和机器学习从业人员提供时间几何深学习。PyTorch地球物理时空学是在Pytorch生态系统现有图书馆、精简的神经网络层定义、用于批量的瞬间快照生成器和综合基准数据集的基础上建立的。这些特征以类似案例的教益性研究加以说明。实验表明图书馆所执行的模型在流行病学预测、马航需求预测和网络交通管理等真实世界问题上的预测性表现。我们对运行时间的敏感性分析表明,该框架有可能在具有丰富时间特征和空间结构的网络规模数据集上运作。