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.Comment: Preprint submitted to Artificial Intelligence. arXiv admin note:
substantial text overlap with arXiv:2211.1470