Graph deep learning methods have become popular tools to process collections of correlated time series. Unlike traditional multivariate forecasting methods, graph-based predictors leverage pairwise relationships by conditioning forecasts on graphs spanning the time series collection. The conditioning takes the form of architectural inductive biases on the forecasting architecture, resulting in a family of models called spatiotemporal graph neural networks. These biases allow for training global forecasting models on large collections of time series while localizing predictions w.r.t. each element in the set (nodes) by accounting for correlations among them (edges). Recent advances in graph neural networks and deep learning for time series forecasting make the adoption of such processing framework appealing and timely. However, most studies focus on refining existing architectures by exploiting modern deep-learning practices. Conversely, foundational and methodological aspects have not been subject to systematic investigation. To fill this void, this tutorial paper aims to introduce a comprehensive methodological framework formalizing the forecasting problem and providing design principles for graph-based predictors, as well as methods to assess their performance. In addition, together with an overview of the field, we provide design guidelines and best practices, as well as an in-depth discussion of open challenges and future directions.

中文翻译：

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用于时间序列预测的图形深度学习

图形深度学习方法已成为处理相关时间序列集合的流行工具。与传统的多变量预测方法不同，基于图形的预测器通过在跨时间序列集合的图形上调节预测来利用成对关系。条件反射采用预测架构上的架构归纳偏差的形式，从而产生一系列称为时空图神经网络的模型。这些偏差允许在大型时间序列集合上训练全局预测模型，同时通过考虑它们之间的相关性（边缘）来定位集合中的每个元素（节点）的预测。用于时间序列预测的图形神经网络和深度学习的最新进展使得采用这种处理框架具有吸引力和及时性。然而，大多数研究都集中在通过利用现代深度学习实践来改进现有架构。相反，基础和方法方面尚未进行系统调查。为了填补这一空白，本教程论文旨在引入一个全面的方法框架，将预测问题形式化，并为基于图的预测器提供设计原则，以及评估其性能的方法。此外，除了该领域的概述外，我们还提供了设计指南和最佳实践，并深入讨论了开放的挑战和未来方向。