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In recent years, many spatial-temporal graph convolutional network (STGCN)models are proposed to deal with the spatial-temporal network data forecastingproblem. These STGCN models have their own advantages, i.e., each of them putsforward many effective operations and achieves good prediction results in thereal applications. If users can effectively utilize and combine these excellentoperations integrating the advantages of existing models, then they may obtainmore effective STGCN models thus create greater value using existing work.However, they fail to do so due to the lack of domain knowledge, and there islack of automated system to help users to achieve this goal. In this paper, wefill this gap and propose Auto-STGCN algorithm, which makes use of existingmodels to automatically explore high-performance STGCN model for specificscenarios. Specifically, we design Unified-STGCN framework, which summarizesthe operations of existing architectures, and use parameters to control theusage and characteristic attributes of each operation, so as to realize theparameterized representation of the STGCN architecture and the reorganizationand fusion of advantages. Then, we present Auto-STGCN, an optimization methodbased on reinforcement learning, to quickly search the parameter search spaceprovided by Unified-STGCN, and generate optimal STGCN models automatically.Extensive experiments on real-world benchmark datasets show that our Auto-STGCNcan find STGCN models superior to existing STGCN models with heuristicparameters, which demonstrates the effectiveness of our proposed method.

Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results