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：作为多变时间序列预测的卡通性任务,Spatio-时空建模是AI 社区的一个重要研究课题。为了解决图形流中隐含的基本异质性和非常态性,我们在本研究中提议Spatio-Tentoral Met-Graph 学习作为关于时空数据的新型图表结构学习机制。具体地说,我们通过将Meta-Node Bank所赋予的Meta-Graph 学习器插入 GCRN encoder-decoder, 将这一想法落实到Mega-Graph 经常网络(MegaCRN)中。此外,我们通过一系列定性评估,对两个基准数据集(METR-LA和PEMS-BAY)和包含非静态现象的大型时空结构数据集(patio-stalalal)进行了全面评价。我们的模型超越了所有三个数据集(超过27%的MACRE和34%的RME)的状态。此外,我们还通过一系列的定性评估,我们展示了我们的模型和不同时空的变式模型/变式模型。