Longji Huang
ABSTRACT
Due to sensor failure or power failure, the spatiotemporal data missing tends to have a greater impact on downstream tasks. Meanwhile, if sensors are scarce, some spatial positions without sensors need data augmentation. Existing workarounds focus on spatial information, often ignoring temporal information, or modeling the spatial and temporal domain separately for imputation. In this paper, we propose Long-term Multidimensional Spatial-Temporal Graph Convolution Network (LMSTGCN), which can not only inductively estimate some missing information, but also achieve data augmentation of target locations. It contains a gated temporal capture module and a multidimensional graph convolution module. The multidimensional graph convolution module can simultaneously model spatial and extra-short term temporal information, and can achieve exponential growth in the range of receptive fields. Corresponding to this module, we designed a spatiotemporal adjacency matrix construction method, which can generate spatiotemporal adjacency matrices of corresponding time lengths as needed. The gated temporal capture module can deal with the short term dependencies in sequences. In experimental analysis, results demonstrate that the proposed model outperforms the state-of-the-art baselines on real-world data sets.
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Cross Ref
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