MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling (2212.05989v2)

Abstract: 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.

• The paper presents MegaCRN, a meta-graph convolutional recurrent network that enhances spatio-temporal modeling efficiency and accuracy.
• It leverages a novel architecture that fuses graph convolutional and recurrent modules to integrate spatial and temporal data effectively.
• Empirical evaluations on benchmark datasets demonstrate significant performance gains over state-of-the-art methods.

Overview of the Paper on Machine Learning and AI

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Methodological Contributions

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Empirical Evaluations

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Theoretical Insights

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Implications and Future Directions

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Concluding Remarks

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