With an ever-increasing number of sensors in modern society, spatio-temporal time series forecasting has become a de facto tool to make informed decisions about the future. Most spatio-temporal forecasting models typically comprise distinct components that learn spatial and temporal dependencies. A common methodology employs some Graph Neural Network (GNN) to capture relations between spatial locations, while another network, such as a recurrent neural network (RNN), learns temporal correlations. By representing every recorded sample as its own node in a graph, rather than all measurements for a particular location as a single node, temporal and spatial information is encoded in a similar manner. In this setting, GNNs can now directly learn both temporal and spatial dependencies, jointly, while also alleviating the need for additional temporal networks. Furthermore, the framework does not require aligned measurements along the temporal dimension, meaning that it also naturally facilitates irregular time series, different sampling frequencies or missing data, without the need for data imputation. To evaluate the proposed methodology, we consider wind speed forecasting as a case study, where our proposed framework outperformed other spatio-temporal models using GNNs with either Transformer or LSTM networks as temporal update functions.

翻译：随着现代社会中传感器数量日益增长，时空时间序列预测已成为面向未来做出明智决策的常用工具。大多数时空预测模型通常包含分别学习空间依赖性与时间依赖性的独立组件。常见方法采用某种图神经网络（GNN）捕捉空间位置间的关系，同时利用另一网络（如循环神经网络RNN）学习时间相关性。通过将每个记录样本表示为图中的独立节点（而非将特定位置的所有测量值聚合为单一节点），时间和空间信息得以采用相似方式编码。在此设定下，GNN可直接联合学习时间与空间依赖关系，同时无需额外的时间网络。此外，该框架不要求沿时间维度的测量值对齐，因此能自然处理不规则时间序列、不同采样频率或数据缺失问题，而无需进行数据插补。为评估所提方法，我们以风速预测为案例研究，结果表明：当使用带有Transformer或LSTM网络作为时间更新函数的GNN时，本框架性能优于其他时空预测模型。