Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the graph streams, in this study, we propose Spatio-Temporal Meta-Graph Learning as a novel Graph Structure Learning mechanism on spatio-temporal data. Specifically, we implement this idea into Meta-Graph Convolutional Recurrent Network (MegaCRN) by plugging the Meta-Graph Learner powered by a Meta-Node Bank into GCRN encoder-decoder. We conduct a comprehensive evaluation on two benchmark datasets (METR-LA and PEMS-BAY) and a large-scale spatio-temporal dataset that contains a variaty of non-stationary phenomena. Our model outperformed the state-of-the-arts to a large degree on all three datasets (over 27% MAE and 34% RMSE). Besides, through a series of qualitative evaluations, we demonstrate that our model can explicitly disentangle locations and time slots with different patterns and be robustly adaptive to different anomalous situations. Codes and datasets are available at https://github.com/deepkashiwa20/MegaCRN.

翻译：时空建模作为多变量时间序列预测的经典任务，一直是人工智能领域的重要研究课题。为应对图数据流中潜在的异质性和非平稳性，本研究提出时空元图学习作为一种新颖的图结构学习机制。具体而言，我们通过将基于元节点库驱动的元图学习器嵌入GCRN编码器-解码器，将该思想实现为元图卷积循环网络（MegaCRN）。我们在两个基准数据集（METR-LA和PEMS-BAY）以及一个包含多种非平稳现象的大规模时空数据集上进行了全面评估。我们的模型在全部三个数据集上均大幅超越现有最优方法（均绝对误差降低超过27%，均方根误差降低超过34%）。此外，通过一系列定性评估，我们证明该模型能够显式解耦具有不同模式的时空位置与时间片段，并对多种异常情境具有稳健的适应性。代码与数据集见https://github.com/deepkashiwa20/MegaCRN。